Employing GPT-4 to realize the DIKWP system-An example of gout diagnosis and treatment

• November 2023

• DOI: 

• 10.13140/RG.2.2.14409.31842/2

• Yucong Duan

 Employing GPT-4 to realize the DIKWP system-An example of gout diagnosis and treatment

Yucong Duan

DIKWP-AC Artificial Consciousness Laboratory

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

DIKWP research group, Hainan University

duanyucong@hotmail.com

Introduction

In the field of healthcare, accurate diagnosis and the development of effective treatment programs are key to improving the quality of life of patients. As a common metabolic disease, the treatment of gout requires not only doctors' professional knowledge but also a comprehensive understanding of the patient's condition.The DIKWP model provides a structured approach to handle such complex medical information[1]. In this presentation, we will take the diagnosis and treatment process of gout as an example, and discuss how to apply the DIKWP model to optimize doctor-patient interaction and improve the efficiency and quality of diagnosis and treatment[2].

Introduction to the DIKWP concept

In today's information-based society, Data, Information, Knowledge, Wisdom and Purpose (DIKWP) forms a core component of the field of Knowledge Management and Artificial Intelligence[3]. The aim of this presentation is to delve into the concept of DIKWP and based on this, to analyse its mathematical understanding in the semantic space, conceptual type space and conceptual instance space of linguistics and its application in the real world[4].

Distinguishing and linking data and information

Data (Data) is an objective description of the real world; it is a record of facts, such as numbers, words and symbols[5]. Data itself contains no direct indicative meaning, but it is the building block of information[6]. In mathematical understanding, data can be thought of as elements in sets that represent figurative representations of what we perceive to be the "same" semantics[7]. For example, a set of temperature readings is data that describes a specific temperature value in a particular environment.

Information is the meaningful output of processed and organised data, which represents the expression of 'different' semantics in the data[8]. Mathematically, information can be defined by the relationships and differences between data[9]. For example, the average, range, or trend of temperature readings are all information, and they provide insights that go beyond the mere data.

Knowledge formation and application

Knowledge is the further refinement and understanding of information that creates a deep insight into the world through connections, laws, pattern recognition and lessons learnt[10]. In a mathematical model, Knowledge can be viewed as a function based on a collection of information that explains the connections between the information and the principles behind it[11]. For example, by analysing multiple temperature readings, we may be able to summarise the pattern of change in temperature at different times of the day, and this pattern constitutes knowledge.

The Nature and Use of Wisdom

Wisdom is a deep application of knowledge that takes into account information about values, ethics and morals[12]. Wisdom is difficult to quantify mathematically, but can be approximated by decision-making models that are based on knowledge and information while incorporating value judgements and desired goals[13]. For example, wisdom may guide us in choosing the right air-conditioning temperature in hot weather in order to reduce energy consumption.

Meaning and Processing of Purpose

The purpose (Purpose) is the final output of the DIKWP model, which represents our understanding of a particular phenomenon (input) and our goal (output)[14]. Mathematically, purpose can be modelled as a function or mapping that transforms the input DIKWP content into a specific goal or outcome[15]. For example, in natural language processing, purpose recognition is based on a user's input (e.g., an instruction or a query) to produce an output (e.g., to perform a task or provide information) that satisfies the user's needs.

DIKWP's real-world use cases

Enterprise Decision Support System: Enterprises use data mining and analysis of market data (Data) to obtain information about market trends, and then convert this information into Knowledge, such as predicting future market changes. Business leaders use this knowledge, combined with the company's values and ethical standards (Wisdom), to make strategic decisions (Purpose), such as whether to enter new markets or develop new products.

Healthcare: Healthcare providers collect physiological data from patients, such as blood pressure and heart rate, and compare and analyse this data to obtain diagnostic information. Doctors use clinical knowledge to diagnose symptoms and then plan treatment for patients based on medical ethics (Purpose).

Intelligent Recommender Systems: Online platforms collect data by tracking users' browsing and purchasing behaviour, and analyse their preferences to generate personalised recommendations (Information). This information is based on the knowledge of user behavioural patterns, and algorithms are used to maximise user satisfaction and platform revenue (Wisdom) in order to achieve business goals (Purpose).

The DIKWP model provides a comprehensive framework for understanding and dealing with real-world problems. It highlights the conversion process from basic data to deep wisdom and shows how this process can be systematised through a mathematical and scientific approach. In the future, with the development of big data and artificial intelligence technologies, the DIKWP model will become increasingly important and will play a key role in guiding decision-making, optimising systems and creating value. Through in-depth research and application of the DIKWP model, we can better understand complex systems and, based on this, drive social and technological progress.

Diving deeper into the DIKWP concept

An in-depth look at the concept of DIKWP, based on which we analyse its mathematical understanding in the semantic space, the conceptual type space and the conceptual instance space of linguistics and its real-world applications.

Distinguishing and linking data and information

Data is an objective description of the real world; it is a record of facts, such as numbers, words and symbols[16]. Data itself contains no direct indicative meaning, but it is the building block of information. In mathematical understanding, data can be thought of as elements in sets that represent figurative representations of what we perceive to be the "same" semantics. For example, a set of temperature readings is data that describes a specific temperature value in a particular environment.

Information is the processed and organised meaningful output of the data, which represents a 'different' semantic representation of the data[17]. Mathematically, information can be defined by the relationships and differences between data. For example, the average, range, or trend of temperature readings are all information, and they provide insights that go beyond the mere data.

Knowledge formation and application

Knowledge is the further refinement and understanding of information that creates a deep insight into the world through connections, laws, pattern recognition and lessons learnt[18]. In a mathematical model, Knowledge can be viewed as a function based on a collection of information that explains the connections between the information and the principles behind it. For example, by analysing multiple temperature readings, we may be able to summarise the pattern of change in temperature at different times of the day, and this pattern constitutes knowledge.

The Nature and Use of Wisdom

Wisdom is a deep application of knowledge that takes into account information about values, ethics and morals[19]. Wisdom is difficult to quantify mathematically, but can be approximated by decision-making models that are based on knowledge and information while incorporating value judgements and desired goals. For example, wisdom may guide us in choosing the right air-conditioning temperature in hot weather in order to reduce energy consumption.

Meaning and Processing of Purpose

The purpose is the final output of the DIKWP model, which represents our understanding of a particular phenomenon (input) and our goal (output)[20]. Mathematically, purpose can be modelled as a function or mapping that transforms the input DIKWP content into a specific goal or outcome[21]. For example, in natural language processing, purpose recognition is based on a user's input (e.g., an instruction or a query) to produce an output (e.g., to perform a task or provide information) that satisfies the user's needs.

DIKWP's real-world use cases

Enterprise Decision Support System: Enterprises use data mining and analysis of market data (Data) to obtain information about market trends, and then convert this information into Knowledge, such as predicting future market changes[22]. Business leaders use this knowledge, combined with the company's values and ethical standards (Wisdom), to make strategic decisions (Purpose), such as whether to enter new markets or develop new products.

Healthcare: Healthcare providers collect physiological data from patients, such as blood pressure and heart rate, and compare and analyse this data to obtain diagnostic information. Doctors use clinical knowledge to diagnose symptoms and then plan treatment for patients based on medical ethics (Purpose).

Intelligent Recommender Systems: Online platforms collect data by tracking users' browsing and purchasing behaviour, and analyse their preferences to generate personalised recommendations (Information). This information is based on the knowledge of user behavioural patterns, and algorithms are used to maximise user satisfaction and platform revenue (Wisdom) in order to achieve business goals (Purpose).

The DIKWP model provides a comprehensive framework for understanding and dealing with real-world problems. It highlights the conversion process from basic data to deep wisdom and shows how this process can be systematised through a mathematical and scientific approach. In the future, with the development of big data and artificial intelligence technologies, the DIKWP model will become increasingly important and will play a key role in guiding decision-making, optimising systems and creating value. Through in-depth research and application of the DIKWP model, we can better understand complex systems and, based on this, drive social and technological progress.

The DIKWP model for cognitive understanding

The aim of this presentation is to elucidate the subjective and objective dimensions of the DIKWP model in cognitive understanding and to provide insights into its application to the semantic space, the conceptual type space and the conceptual instance space of linguistics.

Objectivity and Subjective Refinement of Data

From an objective point of view, data is a direct record of the real world, which consists of raw numbers, texts, images, etc. that we obtain through observation[23]. The subjective process of refining data then involves us categorising and interpreting these raw records to give them a specific meaning[24]. For example, in meteorology, readings of temperature and humidity are objective data, while associating these readings with specific weather phenomena is a subjective refinement.

Information diversity and semantic reconstruction

Information is the processing and organisation of data that reflects the differences and diversity in the data[25]. At the objective level, information is a meaningful combination of data, such as the aggregation of statistical data[26]. At the subjective level, the process of semantic reconstruction of information then involves the further interpretation and understanding of these combinations, enabling us to identify and utilise the intrinsic value of these data.

Integrity of Knowledge and the Construction of Intelligence

Knowledge is a deeper level of understanding based on information, obtained through analysis, comparison and reasoning[27]. It is expressed at the objective level as a set of facts, principles and patterns[28]. From a subjective perspective, the process of intellectual construction of knowledge then requires us to internalize these facts and patterns into a personal cognitive structure that guides our behaviour and decision-making.

Wisdom Ethical Values and Judgements

Wisdom is the application and transformation of knowledge; it is not just the accumulation of information and knowledge, but more importantly the ability to use that knowledge to make insightful decisions[29]. Objectively, wisdom manifests itself as an ability to make efficient decisions based on knowledge[30]. At the subjective level, on the other hand, the ethical values and judgements of wisdom require us to take into account morals and values in our decision-making in order to achieve the greatest social and personal benefit.

Realisation of Purpose and Goal Orientation

Purpose is the ultimate goal of the DIKWP model and describes what we want to achieve by processing information and knowledge[31]. At the objective level, purpose can be seen as a predetermined outcome or output[32]. On the subjective level, the process of realising purpose requires us to apply wisdom in specific environments and situations in order to achieve our goals[33].

Combining the above points, we can see the far-reaching significance of the DIKWP model in knowledge management and information technology[34]. In practice, this model guides us on how to extract useful information from large amounts of data, further transform it into knowledge, and ultimately apply wisdom to make decisions and fulfil our purpose[35]. Whether in the fields of enterprise decision-making, healthcare, intelligent recommender systems, etc., the DIKWP model can help us manage and utilise information more efficiently, and promote social and technological progress[36]. Through in-depth research and application of DIKWP, we will be able to better understand and solve complex problems in the information age[37].

Data collection and analysis

In the medical scenario of gout, data includes medical history, description of symptoms, and blood test results. These raw data are the basis for medical decision-making[38]. By observing and recording these data, doctors form an initial perception of the patient's condition. For example, the specific value of the blood uric acid level is a key piece of data.

Information extraction and application

Information is obtained by interpreting and processing the data[39]. In a gout visit, doctors need to extract key information from blood test results, such as high or low uric acid levels. In addition, the doctor collects information about the patient's lifestyle habits and dietary patterns to aid in diagnosis and treatment planning.

Knowledge Formation and Intellectual Construction

Knowledge is a deep understanding based on information[40]. Doctors combine information about individual patients with medical theories, previous clinical experience and existing medical research findings to form a body of knowledge about gout. This includes not only knowledge of the pathophysiology of gout, but also therapeutic approaches and mechanisms of drug action.

Wisdom Ethical Values and Judgements

Wisdom is a further step up from knowledge, and it involves a comprehensive consideration of the patient's emotional, ethical and social values. In the treatment of gout, wisdom is reflected in how doctors balance the effectiveness of treatment with the patient's quality of life, and how they consider the impact of treatment options on the patient's social and psychological state.

Realisation of Purpose and Goal Orientation

The purpose is the ultimate point of the whole treatment process. In gout treatment, the purpose is not only to alleviate the patient's symptoms, but more importantly to improve the patient's quality of life and reduce the frequency of the disease. Doctors need to consider data, information, knowledge and wisdom to develop a personalised treatment plan for the patient.

Case study of DIKWP application for gout diagnosis and treatment

Application of Data

Mr Zhang, a gout patient, came to the clinic with a series of blood test reports. The first thing the doctor focuses on is the data on uric acid levels in the blood, which is an important basis for diagnosing gout. These data are objective and are not subject to the doctor's subjective judgement.

Information extraction

By asking Mr Zhang about his dietary habits, the doctor learnt that he often consumed high-purine foods, which were directly related to the elevated uric acid levels in his blood. This process extracted valuable information from the data, which helped the doctor further understand the cause of Mr Zhang's gout.

Construction of Knowledge

Based on Mr Zhang's data and information, the doctor diagnosed him with gout, taking into account medical theories. At the same time, the doctor is aware that certain medications can help lower uric acid levels. This knowledge comes from the doctor's medical training and clinical experience.

The use of wisdom

In treating Mr Cheung's gout, doctors need to consider the side effects of the medication, the sustainability of the treatment and the patient's financial situation. The wisdom lies in how to combine these factors to develop a treatment plan that is both effective and suitable for Mr Cheung's personal situation.

Achievement of Purpose

The ultimate goal of the treatment was to control Mr Zhang's gout symptoms and educate him on how to manage his condition through lifestyle changes. The doctors achieved this purpose by developing a comprehensive treatment plan that included medication and life coaching.

Gout Dialogue Scenes

Dialogue Background:

The patient, Mr Cheung, came to the clinic with joint pain and a diagnosis of gout. The doctor is assessing his condition.

Data stage:

Doctor: "Please tell me your age, weight, and the frequency and intensity of your joint pain episodes."

Here the doctor is collecting basic data that is unambiguous and can be directly measured and recorded. Zhang provides specific values: 45 years old, 85kg, about 4 pain episodes per month, and a pain level of 7 on a 10-point pain scale out of 10.

Information stage:

Mr Zhang: "My most recent attack was last night and the pain kept me awake all night."

This is a personal description of Mr Zhang's symptoms. The doctor extracts key information from this description: the severity of the gout attack and its impact on the patient's quality of life.

Knowledge stage:

Doctor: "Based on your description and blood test results, your uric acid level is significantly higher than normal, which is consistent with the symptoms of a gout attack."

The doctor combines the data and information collected with medical knowledge to form a comprehensive understanding of Mr Zhang's condition. This knowledge was obtained through analysis and reasoning at the information level.

Wisdom stage:

Doctor: "Our goal of treatment is to lower your uric acid levels, reduce the frequency of attacks, and minimise your pain. At the same time, we need to consider the impact of treatment on your daily life."

The doctor is using wisdom here, not just providing a treatment plan based on medical knowledge, but a combination of treatment feasibility, patient quality of life, and long-term health goals.

Purpose stage:

Mr Cheung: "I would like to find a treatment plan that can control my condition without interfering with my work."

Here Mr Cheung expresses his expectation of treatment, which is his purpose for treatment. Based on this purpose, the doctor will design a personalised treatment plan.

The process of cognitive change:

As the dialogue progresses, both Chang and the doctor's cognitions change.

Patient's cognitive change:

Mr Zhang initially had a limited understanding of his condition and only perceived the symptoms of gout. Through the dialogue with the doctor, his perception evolved from a simple understanding of the symptoms to an understanding of the causes of gout, to an understanding of the treatments, and finally to a comprehensive understanding of his health condition. He also learnt from his doctor how to manage his condition and developed a plan to work with him to reach common goals.

Cognitive change in the doctor:

The doctor also experienced cognitive changes during the process. Initially, the doctor had only a basic understanding of Mr Cheung's condition. As more data and information were collected, the doctor gained a deeper understanding of Mr Zhang's condition. At the wisdom level, the doctor considered not only the medical aspects, but also the patient's personal life and work needs, resulting in a comprehensive treatment plan. By understanding the patient's purpose, the doctor is able to adjust the treatment plan more precisely so that it is both scientific and relevant to the patient's actual situation.

DIKWP mapping and processing:

Throughout the dialogue, each round of communication between the doctor and Zhang can be seen as a DIKWP cycle. In each cycle, data is collected and converted into information, information is analysed and fused into knowledge, and knowledge is used to form wisdom and ultimately directed towards a clear therapeutic purpose. This series of cycles results in enhanced cognition for both the physician and the patient, ultimately leading to a shared therapeutic goal.

The DIKWP model provides a systematic information processing framework for gout diagnosis and treatment. Through the layers of data, information, knowledge, wisdom, and purpose, doctors and patients are able to communicate efficiently and form effective treatment strategies. This model is not only applicable to gout diagnosis and treatment, but can also be extended to other healthcare scenarios to help healthcare workers and patients make better decisions in complex healthcare environments.

Localised refinement of DIKWP processing

Doctor-Patient Communication Clip: Data Collection

Doctor: "Mr Zhang, please provide specific symptoms from your last attack and your recent dietary habits."

Mr Zhang: "During my last attack, my toes were very painful, especially at night. Recently I have been eating a lot of seafood and drinking beer."

Data stage:

Data mapping at word granularity:

Toes, pain, night, seafood, beer

Data Processing:

Physicians record frequency, intensity, duration, and diet type of symptoms.

Conceptual-Semantic Transformation:

Zhang's description was transformed into quantifiable gout-related data.

Cognitive change process:

Cognitive change in the patient:

Mr Zhang begins to realise that his dietary habits may be associated with gout attacks.

Doctor's cognitive change:

The doctor recognises that the patient's lifestyle habits may be a trigger for symptoms based on Mr Zhang's description.

Doctor-patient communication segment: information extraction

Doctor: "Based on the symptoms and diet you provided, we can see that this is consistent with a typical gout attack pattern."

Information stage:：

Information mapping at word granularity:

Typical, Gout, Seizure Patterns

Information Processing:

Doctors correlate symptoms with dietary habits to identify typical gout triggers.

Conceptual-Semantic Transformation:

Identify links between symptoms and eating habits and correlate them with the medical model of gout.

The process of cognitive change:

Cognitive change in patients:

Mr Cheung has a better understanding of gout triggers.

Doctor's cognitive change:

The doctor analyses the information to gain a clearer understanding of the patient's condition and prepares further diagnostic steps.

Doctor-patient exchange segment: Knowledge formation

Doctor: "Gout is caused by high uric acid in the body, which is produced from the body's metabolism of purines, and seafood and beer are foods high in purines."

Knowledge stage:

Knowledge mapping at word granularity:

Uric acid, metabolism, purines, seafood, beer

Knowledge Processing:

The doctor explains the relationship between the biochemical mechanisms of gout and food sources.

Conceptual-Semantic Transformation:

The metabolic processes of chemicals are associated with symptoms to form knowledge about the causes of gout.

Process of cognitive change:

Cognitive change in the patient:

Mr Cheung has a more accurate knowledge of the biochemistry of gout causes.

Doctor's cognitive change:

The doctor's understanding of the patient's feedback confirms the patient's uptake of knowledge about the causes of gout.

Doctor-patient communication clip: wisdom used

Doctor: "We need to find a balance that reduces the intake of these high-purine foods without affecting your daily life too much."

Wisdom Stage:

Wisdom mapping for word granularity:

Balance, Reduce, High Purine, Intake, Impact, Daily Life

Wisdom processing:

Physicians consider the practical feasibility of treatment options for patients' lives.

Conceptual-Semantic Transformation:

Blending medical advice with the practicalities of the patient's life results in intelligent decision-making.

Cognition change process:

The patient's cognition change:

Mr. Zhang begins to think about how to adjust his eating habits to manage his condition.

Doctor's cognition change:

The doctor adapts the treatment recommendations to the patient's life situation to ensure that the patient can accept and follow them.

Doctor-patient communication segment: Reaching the Purpose

Mr. Zhang: "I want to be able to manage my gout symptoms without giving up my favorite foods."

Purpose Stage:

Purpose mapping at word granularity:

Control, gout, symptoms, don't give up, like, food

Purpose processing:

The doctor and Mr. Zhang discuss and determine a treatment plan that meets both the medical needs and the patient's personal preferences.

Conceptual-Semantic Transformation:

The patient's life preferences are combined with treatment goals to form a shared treatment purpose.

Cognition change process:

Patient's cognition change:

Mr. Zhang understands that complete avoidance of certain foods may be unrealistic, but he recognizes that moderate control is feasible.

Physician's cognition change:

The physician recognizes the need to consider the patient's personal preferences and lifestyle habits in the treatment plan.

Algorithm design and implementation

Designing algorithms to handle data flow in DIKWP models typically involves natural language processing (NLP), data analysis, knowledge extraction, wisdom decision making, and goal-directed planning. The following is a conceptual algorithm design that demonstrates how the above processes can be combined to process DIKWP elements in doctor-patient communication.

Algorithm Overview

The algorithm will be executed through the following steps:

Data Extraction

Information Processing

Knowledge Construction

Wisdom Decision-Making

Purpose Identification

Algorithm Detailed Design

1. Data Extraction

Purpose: Recognize and extract objective data from natural language communication.

Input: doctor-patient conversation text Output: structured dataset

Steps:

plaintextCopy codefunction extractData(dialogue):

    dataPoints = []

    for sentence in dialogue:

        entities = NLP.extractEntities(sentence)

        for entity in entities:

            if entity.type in [NUMERIC, DATE, TIME]:

                dataPoints.append(entity)

    return dataPoints

2. Information Processing

Purpose: To transform extracted data into useful information.

Input: structured dataset Output: information objects

Steps:

plaintextCopy codefunction processData(dataPoints):

    information = []

    for dataPoint in dataPoints:

        context = getContext(dataPoint)

        relation = getRelation(dataPoint, context)

        information.append({'entity': dataPoint, 'context': context, 'relation': relation})

    return information

3. Knowledge Construction

Purpose: Integrate information to build knowledge.

Input: information objects Output: knowledge map

Steps:

plaintextCopy codefunction constructKnowledge(information):

    knowledgeGraph = KnowledgeGraph()

    for info in information:

        knowledgeGraph.addNode(info.entity)

        knowledgeGraph.addEdge(info.entity, info.relation, info.context)

    return knowledgeGraph

4. Wisdom Decision-Making

Purpose: Wisdom decision making based on knowledge graphs.

Input: knowledge graph Output: decision making program

Steps:

plaintextCopy codefunction makeWisdomDecision(knowledgeGraph, patientPreferences):

    decisionTree = DecisionTree()

    for node in knowledgeGraph.nodes():

        decisionFactors = evaluateDecisionFactors(node)

        if isEthicallyAligned(decisionFactors, patientPreferences):

            decisionTree.addDecision(node, decisionFactors)

    bestDecision = decisionTree.getBestDecision()

    return bestDecision

5. Purpose Identification

Purpose: Identify patient's purpose and align it with treatment goals.

Input: decision-making scenarios, patient's purpose Output: individualized treatment plan

Steps:

plaintextCopy codefunction identifyPurpose(decision, patientIntent):

    treatmentPlan = TreatmentPlan()

    alignmentScore = align(decision, patientIntent)

    if alignmentScore > threshold:

        treatmentPlan.setPlan(decision)

    else:

        treatmentPlan.modifyPlan(decision, patientIntent)

    return treatmentPlan

Realization points

NLP.extractEntities(sentence)：Use natural language processing tools (e.g. spaCy, NLTK) to recognize and extract entities and values in sentences.

getContext(dataPoint)：Analyze the text around the data point to determine the context.

getRelation(dataPoint, context)：Determine the relationship between a data point and its context.

KnowledgeGraph()：Construct a knowledge graph where nodes are entities and edges represent relationships between entities.

evaluateDecisionFactors(node)：Evaluate decision-making factors such as patient preference, treatment efficacy, and side effects.

isEthicallyAligned(decisionFactors, patientPreferences)：Ensure that decision-making takes into account the patient's ethical preferences.

DecisionTree()：Decision tree modeling for decision analysis.

align(decision, patientIntent)：Aligning Decision Options with Patient Purpose.

TreatmentPlan()：Create an adjustable treatment plan model.

This algorithm design provides a conceptual framework for processing DIKWP models from natural language communication. Each step needs to be further specified and optimized for application in real healthcare settings. For large-scale implementations, technical issues such as algorithmic efficiency, storage structure, user interface and real-time data processing also need to be considered.

DIKWP processing solutions interfaced with GPT4

To design a DIKWP processing solution that interfaces with GPT-4, we need to consider how to automate the processing of data, information, knowledge, wisdom and purpose through the GPT-4 processing flow. Below is an overview of the solution:

Solution Design

1. Data Extraction

Purpose: Extract specific data points from user input.

Achievement: Use the natural language understanding capabilities of the GPT-4 to recognize and extract key data points. For example, user input such as medical symptom descriptions, numerical information, etc.

2. Information Processing

Purpose: Analyze extracted data to distill useful information.

Achievement: Utilize the pattern recognition capabilities of the GPT-4 to identify correlations between data and generate summaries of information. For example, correlate symptoms with possible diseases.

3. Knowledge Construction

Purpose: Combine general and domain-specific knowledge to develop a comprehensive understanding of a topic.

Achievement: Use GPT-4's knowledge base and learning capabilities to integrate relevant information and construct a certain body of knowledge. For example, combining medical information with clinical guidelines to construct disease diagnosis and treatment knowledge.

4. Wisdom Decision-Making

Purpose: To make informed decisions based on knowledge.

Achievement: Provide decision-making recommendations that take into account ethics, morality, and the patient's personal situation through the GPT-4's reasoning and decision-support functions. For example, consider the feasibility of treatment options and the patient's quality of life.

5. Purpose Identification

Purpose: Determine the user's purpose and form the final goal-oriented output.

Achievement: Utilize the deep learning capabilities of the GPT-4 to understand the user's purpose from the interaction and generate a responsive goal-directed plan. For example, a patient wants to find a treatment plan that controls symptoms and fits the individual's lifestyle.

Technical Implementation Steps

User Interface (UI): Design a user-friendly interface that allows patients and doctors to enter natural language descriptions.

API Calls: Design a system back-end to interact with GPT-4 through APIs provided by OpenAI.

Processing Flow:

Receive user input.

Call GPT-4 API for data extraction and information processing.

Combine with the built-in medical knowledge base for knowledge construction via GPT-4.

Consider medical ethics and individualized patient needs when processing intelligent decisions.

Recognize the user's purpose and give the final recommendation or plan.

Feedback Mechanism: Design a feedback loop that allows the user to evaluate the provided solution and the system to iteratively learn and improve accordingly.

Expected Output

Structured data describing symptoms

Possible diagnostic information

Treatment recommendations

Individualized treatment plans

Alignment of user purpose to system goals

Next steps

Validation and Testing: Test the solution in real healthcare environments to validate its effectiveness and accuracy.

User Training: Provide training to healthcare professionals and patients on how to interact with the system.

Data Privacy and Security: Ensure that the solution complies with regulations and standards for healthcare data processing.

Combined with GPT-4's high-level language understanding and generation capabilities, the DIKWP processing solution provides powerful support in the healthcare field, giving effective assistance from simple data extraction to complex decision making. Through continuous learning and adjustment, this system is expected to play an important role in the perspective of improving the quality and efficiency of healthcare.

Focus and Difficulty Analysis

Designing a DIKWP processing solution that interfaces with GPT-4 requires consideration of how to automate the processing of data, information, knowledge, wisdom and purpose in the processing flow. Below is a solution for a medical consultation scenario (e.g., gout diagnosis) focusing on natural language understanding, contextual analysis, knowledge reasoning, ethical considerations, and purpose understanding.

Validation and Testing: Test the solution in real-world healthcare environments to validate its effectiveness and accuracy.

User Training: Provide training to healthcare professionals and patients on how to interact with the system.

Data Privacy and Security: Ensure that the solution is compliant with healthcare data processing regulations and standards.

Combined with GPT-4's high-level language understanding and generation capabilities, the DIKWP processing solution provides powerful support in the healthcare field, giving effective assistance from simple data extraction to complex decision making. Through continuous learning and adjustment, this system is expected to play an important role in improving the quality and efficiency of healthcare.

Focus and Difficulty Analysis

Designing a DIKWP processing solution that interfaces with GPT-4 requires consideration of how to automate the processing of data, information, knowledge, wisdom, and purpose in the processing flow. Below is a solution for a medical consulting scenario (e.g., gout diagnosis) that focuses on natural language understanding, contextual analysis, knowledge reasoning, ethical considerations, and intent understanding.

System Design Overview

Data Extraction Module

Functional Description: Extract specific data points, such as symptoms, time, frequency, etc., from natural language descriptions provided by the user.

Difficulty Analysis:

Need to accurately recognize medical terminology and related data in patient descriptions.

Medical data may contain ambiguous expressions that require contextual understanding.

Information Processing

Function: Transform extracted data into useful information to build a preliminary picture of the patient's condition.

Difficulty Analysis:

Extraction of key information requires an understanding of the intrinsic connections of the medical data, such as the association of symptoms with possible diseases.

The processed information needs to be accurate, as medical decision making requires high quality information.

Knowledge Construction

Functional Description: Integration of general knowledge base and domain-specific medical knowledge to form a comprehensive understanding of the condition.

Difficulty Analysis:

Combining the knowledge base of GPT-4 and medical databases for in-depth knowledge reasoning faces the challenges of knowledge timeliness and accuracy.

Medical knowledge is rapidly updated, and the system needs to continuously learn the latest medical research results.

Wisdom Decision-Making Module

Functional Description: Apply wisdom to decision-making based on knowledge, taking into account factors such as ethics, patient preference, and treatment effectiveness.

Difficulty Analysis:

Decision-making at the wisdom level requires modeling human moral and ethical judgments, which is challenging in terms of algorithm design.

Learning from historical data is needed to simulate the decision-making process of experienced physicians.

Purpose Identification Module

Functional Description:Identifies and understands the user's purpose and generates a goal-oriented action plan.

Difficulty Analysis:

User purpose can be ambiguous and requires advanced natural language understanding and sentiment analysis skills.

Purpose identification requires not only understanding the literal meaning, but also grasping the meaning and long-term goals.

Technical implementation details

Data Extraction module

Key Technology: Natural Language Processing (NLP), especially Named Entity Recognition (NER).

Implementation Methods:

Identify medical terms and key data points using NLP libraries (e.g. spaCy, OpenAI API).

Perform entity normalisation to map non-standard terms to standard medical entities.

Information Processing

KEY TECHNIQUES: Contextual analysis and correlation inference.

Realisation method:

Combining semantic analysis techniques to understand the inherent logical relationships between data points.

Using the pattern recognition capabilities provided by the GPT-4, information summaries and patient condition portraits are generated.

Knowledge Construction

Key technologies: Knowledge Graph and Medical Reasoning.

Realisation method:

The knowledge graph was constructed through the knowledge base of GPT-4 as well as external medical databases.

Make inferences that link symptoms, test results, and potential medical diagnoses.

Wisdom Decision-Making

Key technologies: Multi-objective optimisation and ethical computing models.

Realisation method:

Design of a decision support system that mimics the decision-making process of a human physician, taking into account treatment outcomes, patient preferences, and patient quality of life.

Evaluate and optimise possible decisions using historical cases and ethical guidelines.

Purpose Identification

Key technologies: Purpose recognition and user behaviour analysis.

Realisation method:

Utilise deep learning models such as Recurrent Neural Networks (RNN) or transformer models to understand the user's natural language input.

Combine this with sentiment analysis to understand the user's emotional state and purpose.

Implementation steps

User Input: Design the user interface to collect patient descriptions and physician queries.

Call GPT-4 API: Send user input to GPT-4 and receive processed output.

Post-processing: Post-processing of the GPT-4 output to distil key information required for decision making.

Feedback loop: The user evaluates the provided solution and the system optimises itself based on the feedback.

Output

Structured healthcare data reporting.

Diagnostic information and patient condition analysis.

Wisdom decision support, including treatment options and possible side effects.

Personalised treatment recommendations aligned with patient purpose.

The success of the entire solution relies on the combination of GPT-4's powerful natural language processing capabilities, knowledge reasoning and decision support features. The focus is on addressing the complexity of data, information and knowledge processing in healthcare, taking into account individual patient needs and ethical considerations. With such a solution, the quality of healthcare decisions can be improved, patient satisfaction enhanced and personalised healthcare driven.

Detailed explanation of the internal processing of the system

As a system with DIKWP processing capabilities, I will simulate the internal processing from the perspective of a software engineer. A medical diagnostic scenario will be used, in which the communication between a gout patient and a doctor will be analysed and processed. The process described here is conceptual and simplifies the complexity of the real world.

Overview of processing within the system

Receiving input: The system receives the dialogue between the doctor and patient as input.

Data extraction: Data about symptoms and personal information are extracted from the dialogue.

Information Processing: The extracted data is converted into information that identifies symptoms and possible causal relationships.

Knowledge construction: Constructs knowledge about gout based on the information and the built-in medical knowledge base.

Wisdom Decision Making: Use the constructed knowledge to form intelligent decisions that take into account the patient's specific situation and preferences.

Purpose Understanding: Determine the patient's treatment purpose and generate personalised treatment recommendations.

Output generation: Provide decision support and treatment recommendations to physicians and patients.

Detailed internal processing steps

1. Receiving input

The system initialises and waits for user input. When a dialogue between a patient and a doctor is entered into the system, it is first captured and stored as raw text.

2. Data extraction

The system first uses Natural Language Processing (NLP) techniques to analyse the dialogue text. Through the Named Entity Recognition (NER) module, the system is able to identify key medical entities such as symptoms ("toe pain"), time of day ("at night"), frequency ("about 4 times a month "), and associated lifestyle habits ("frequently eats seafood and drinks beer").

plaintextCopy codefunction extractData(text):

    entities = NER.process(text)

    data = {

        'symptoms': [e.text for e in entities if e.type == 'SYMPTOM'],

        'habits': [e.text for e in entities if e.type == 'HABIT'],

        ...

    }

    return data

3. Information processing

Next, the information processing module analyses the data points, determines the relationships between them and transforms them into useful information. For example, it may combine "toe pain" and "nighttime" to form a unit of information indicating that symptoms are worse at night. In addition, the system will also recognise the potential relationship between seafood and beer and gout attacks.

plaintextCopy codefunction processInformation(data):

    info = []

    for symptom in data['symptoms']:

        info.append(f'Increased pain in toe, especially during night times')

    for habit in data['habits']:

        info.append(f'High purine intake from seafood and beer could contribute to symptoms')

    ...

    return info

4. Knowledge building

The knowledge building module combines information with an in-built medical knowledge base to form expertise about gout. This may include clinical pathways, treatment options and medication information for gout. At this stage, the system also considers medical guidelines and best practices to ensure that the knowledge provided is current and authoritative.

plaintextCopy codefunction constructKnowledge(info):

    knowledge = {

        'diagnosis': 'The symptoms and habits suggest a diagnosis of gout',

        'treatment': 'Recommended to reduce purine intake and consider medication to lower uric acid levels',

        ...

    }

    return knowledge

5. Wisdom decision-making

The Wisdom Decision Making module uses constructed knowledge and takes into account the patient's individual circumstances (e.g., quality of life and preferences) to formulate treatment recommendations. The system evaluates the pros and cons of different treatment options, taking into account patient acceptability and treatment compliance.

plaintextCopy codefunction makeWisdomDecision(knowledge, patientPreferences):

    decisions = []

    if patientPreferences['lifestyle_change'] is acceptable:

        decisions.append('Lifestyle modification to manage gout')

    if patientPreferences['medication'] is acceptable:

        decisions.append('Consider medication to control uric acid levels')

    ...

    return decisions

6. Purpose understanding

In terms of understanding the patient's purpose, the system needs to capture the patient's goals and expectations from the dialogue. For example, if a patient expresses a desire to avoid side effects, the system will take this as an important factor in generating treatment recommendations.

plaintextCopy codefunction identifyPurpose(decisions, dialogue):

    patientIntent = NLP.extractIntent(dialogue)

    alignedDecisions = []

    for decision in decisions:

        if decision aligns with patientIntent:

            alignedDecisions.append(decision)

    ...

    return alignedDecisions

7. Output generation

Finally, the system integrates the results of the processing into recommendations that can be understood and used by doctors and patients. It generates a structured report containing diagnostic information, treatment recommendations, and a personalised treatment plan adapted to the patient's preferences.

plaintextCopy codefunction generateOutput(knowledge, decisions):

    report = {

        'Diagnosis': knowledge['diagnosis'],

        'Treatment Recommendations': decisions,

        ...

    }

    return report

System Iteration and Learning

The system iterates and learns through user feedback. Over time, it adapts its decision-making algorithms based on patient outcomes and satisfaction, continuously improving the accuracy and personalisation of its wisdom decisions.

This system is an idealised example of automated DIKWP processing in the field of medical consultations. It demonstrates how complex medical dialogues can be transformed into structured treatment recommendations. In practice, this system needs to be rigorously tested and validated to ensure its accuracy and reliability. Through continuous optimisation and learning, it is expected to play an important role in improving the quality of treatment, enhancing patient experience and optimising the allocation of healthcare resources.

Communicate only in DIKWP as a language

In the DIKWP model, we will build communication based on data, information, knowledge, wisdom and purpose. The following is an example of DIKWP communication for a gout diagnosis and treatment scenario.

Data (D)

Age: 45 years old

Weight: 85kg

Frequency of pain episodes: 4 per month

Pain intensity: 7/10

Diet: Frequent intake of seafood and beer

Blood uric acid level: Higher than normal

Information (I)

Symptoms match the pattern of a typical gout attack

Pain worsens at night

High purine food intake is strongly associated with gouty attacks

Knowledge (K)

Gout is caused by abnormal blood uric acid levels

High purine content in seafood and beer may lead to elevated blood uric acid levels

Lower purine intake and appropriate medication can control the condition

Wisdom (W)

Consider the balance between patient quality of life and treatment options

Trade-off between patient preference and treatment efficacy

Consider sustainability of patient lifestyle habits while reducing purine intake

Purpose (P)

To control the symptoms of gout

Maintain normal living and working conditions

Develop an acceptable dietary modification plan without completely abandoning the patient's preferred foods

In DIKWP-based communication, we focus on the content of the communication rather than the fluent expression of natural language. This type of communication focuses more on the precise delivery of information and is suitable for scenarios that require precise data and decision-making, such as medical diagnosis. With the framework of the DIKWP model, we are able to build a clear, structured process for information delivery and processing.

DIKWP Processing System Based on GPT-4

As a software engineer, you need to focus on the following aspects if you want to implement a GPT-4 based DIKWP processing system:

Data (D)

Collection: extract specific data points from user inputs, such as age, weight, pain frequency, etc.

Implementation: use the entity recognition feature of GPT-4 to extract these data points from the user's description.

Information (I)

Extraction: Generate information about the patient's condition, such as severity of symptoms and possible triggers, based on the extracted data points.

Implementation: Use the pattern recognition capabilities of the GPT-4 to extract meaningful information based on the data points.

Knowledge (K)

Build: combine information with a medical knowledge base to build expertise about gout, including causes and treatments.

Implementation: use the deep learning capability of GPT-4 to combine external knowledge bases for knowledge inference.

Wisdom (W)

Decision-making: Use wisdom to form treatment decisions, which takes into account the patient's individual circumstances and the impact of treatment options.

Achievement: Evaluate the advantages and disadvantages of different treatment options through the GPT-4's reasoning and contextual analysis capabilities.

Purpose (P)

Purpose comprehension: determine the patient's treatment purpose, including preferences for treatment options and desired outcomes.

Implementation: Deep semantic analysis using GPT-4 to extract patient's purpose from the dialogue and generate a response.

System implementation process:

User Interaction Interface: Create an interface where users can enter natural language descriptions.

GPT-4 Integration: Integrate the GPT-4 API into the backend to process user input.

Data Processing: Design a pipeline to extract structured information from the data returned by GPT-4.

Knowledge Integration: Combine the extracted information with the medical knowledge base to build knowledge through GPT-4.

Decision Algorithms: Develop algorithms to use the knowledge generated by GPT-4 to form wisdom-based decisions.

Purpose Matching: Analyse the user's purpose using GPT-4 and match it to the treatment plan.

Output Generation: Design templates to generate the final output of the system, including diagnosis, recommendations, and plans.

Technical Considerations:

Data accuracy: ensure that the data extracted from user descriptions is accurate.

Information completeness: the information extracted from the data needs to be comprehensive.

Knowledge update: the knowledge base used by the system should be updated regularly.

Wisdom Decision Making: Decisions should take into account the patient's individual quality of life and medical outcomes.

Purpose Recognition: The purpose recognition module needs to be intelligent enough to understand complex and ambiguous user wisdom.

Output and Feedback:

User feedback: collecting user feedback on system outputs for continuous optimisation of system performance.

System Iteration: iteratively improve data extraction, information processing, knowledge building, wisdom decision making and purpose understanding modules based on feedback.

When developing a GPT-4-based DIKWP processing system, software engineers need to consider how to design user interactions, integrate GPT-4 APIs, process and integrate data, build knowledge, form wisdom decisions, and understand user purpose. Through continuous iteration and optimisation, such a system can play an important role in providing precision medical advice.

Feasibility Analysis

In assessing the feasibility of interfacing with GPT-4 to implement the DIKWP processing system described above, we need to consider multiple dimensions, including technical implementation, design challenges, data privacy and security, user experience, and system sustainability. A detailed feasibility assessment is presented below.

Feasibility of Technology Implementation

Natural Language Processing (NLP) technology:

The core strength of GPT-4 is its advanced NLP capabilities, which provide a strong foundation for parsing doctor-patient dialogues.GPT-4 is able to understand complex medical terminology and natural language descriptions, which makes it feasible to extract data and information from the dialogue.

Difficulties include recognition accuracy of specific medical terms and accurate parsing of ambiguous expressions. These issues may require additional custom model training and fine tuning.

Data Extraction and Processing:

It is technically feasible to extract data from natural language text using the entity recognition capabilities of the GPT-4.The GPT-4 can be trained or tuned to recognise specific data patterns, thereby improving the accuracy of data extraction.

Pattern recognition and correlation analysis involved in information processing fall within the current capabilities of GPT-4. However, ensuring the medical accuracy of information may necessitate the integration of external medical knowledge repositories.

Knowledge Construction and Wisdom  Decision-Making：

The knowledge construction capability of GPT-4 is achieved through its extensive training dataset, indicating its potential to construct complex medical knowledge graphs. However, given the continuous updates in the medical field, GPT-4 may need to synchronize with the latest medical research and guidelines. Wise decision-making requires consideration of ethics and individual patient circumstances, posing greater challenges technically. While GPT-4 can offer suggestions, the ultimate decision may still rely on the professional judgment of the healthcare provider.

Purpose Understanding: 

GPT-4 can be utilized for understanding and interpreting user purpose. However, the complexity of purpose recognition in a healthcare setting demands rigorous scrutiny of GPT-4's outputs to ensure alignment with the actual purposes of patients.

Design Challenges

User Interface (UI) Design:

An intuitive and user-friendly UI is crucial for facilitating effective interaction between users and the system. Designing a UI that guides users to provide necessary data and express their purposes is a challenge. Additionally, UI design must adhere to the standards and best practices of medical software to ensure that the user experience aligns with the requirements of the clinical environment.

System Integration: 

Integrating GPT-4 with medical knowledge bases and healthcare record systems is key to realizing the DIKWP processing system. This necessitates addressing issues such as data formats, API compatibility, and system interoperability.

Explainability and Transparency:

Medical decision-making systems must offer interpretable recommendations. Given that the decision-making process of GPT-4 may be perceived as a "black-box," enhancing transparency and interpretability poses a significant challenge in the design process.

Data Privacy and Security

Sensitivity of Medical Data:

Handling medical data requires compliance with regulations such as HIPAA to ensure the confidentiality and security of patient information.

Integrated solutions with GPT-4 must be designed to incorporate security measures such as data encryption, access control, and audit tracking.

Data Sharing and Storage:

When exchanging data with GPT-4, it is crucial to ensure that patient data is not accessed or stored by unauthorized third parties.

User Experience

Interactivity and Feedback:

Users (both patients and doctors) should be able to engage in two-way interactions with the system, providing feedback to enhance the accuracy and efficiency of the system. The system should be capable of addressing user queries and providing timely feedback and clarification.

Training and Support:

Users require training to effectively use the system, including an understanding of its capabilities and limitations.

Providing user support and educational materials will be crucial to ensuring the successful deployment and adoption of the system.

Sustainability of the System

Continuous Learning and Updating:

The system design should allow GPT-4 to self-update and learn based on the latest medical research, ensuring the timeliness of knowledge.

Monitoring and assessing system performance are necessary for continuous improvement in decision quality and user experience.

Cost-Benefit Analysis:

The costs associated with implementing such a system need to be compared with its potential medical benefits to determine its economic feasibility.

In the long term, the cost-effectiveness of the system will be influenced by its impact on improving diagnostic and treatment quality, enhancing patient satisfaction, and optimizing the allocation of medical resources.

Feasibility of Technical Implementation

Natural Language Processing (NLP) Technology:

GPT-4 demonstrates robust capabilities in understanding and generating natural language, providing a solid foundation for parsing medical dialogues. Particularly, it exhibits significant advantages over other models in recognizing and understanding complex medical terms, although further training and fine-tuning may be necessary for specific medical terminology recognition. Additionally, GPT-4 shows strong competence in understanding ambiguous expressions, yet this remains a challenge in the field of NLP.

Data Extraction and Processing:

Utilizing GPT-4 for extracting data from natural language texts is technically feasible, but considerations need to be made on how to optimize the model to enhance the accuracy of recognizing specific data patterns. In information processing, GPT-4 already possesses powerful pattern recognition and correlational analysis capabilities. Given the requirements for medical accuracy and external medical knowledge databases, this aspect may necessitate additional integration and custom development.

Knowledge Construction and Intelligent Decision-Making:

The capability of GPT-4 in knowledge construction is notable, yet the real-time update and reliability of medical knowledge pose a challenge. At the level of intelligent decision-making, considering the complexity of ethics and personal preferences, further input and verification from medical professionals may be required.

Purpose Understanding:

Understanding and interpreting the purpose of users (patients) represent another potential application scenario for GPT-4. In the practical medical environment, this demands highly accurate and reliable model outputs, especially when dealing with complex and ambiguous purposes.

Design Challenges

User Interface (UI) Design:

Effective user interaction is crucial for UI design. The UI design for medical software necessitates high precision and user-friendliness, making it a critical consideration in the design process.

System Integration:

Integrating GPT-4 with existing medical knowledge bases and medical record systems is a complex engineering task that requires addressing technical compatibility and data interoperability issues.

Explainability and Transparency:

Medical decision-making demands a high level of explainability and transparency. As the decision process of GPT-4 may not be entirely transparent, developing additional explanatory interfaces or functionalities may be necessary.

Data Privacy and Security

Sensitivity of Medical Data:

The processing of medical data must adhere to strict regulations, such as HIPAA. Design considerations should include security measures like data encryption, access control, and audit tracking.

Data Sharing and Storage:

When exchanging data with GPT-4, safeguarding patient data from unauthorized access or storage is paramount.

User Experience

Interactivity and Feedback:

The key to user experience lies in the system's ability to facilitate effective two-way interaction, timely feedback, and clarification with the user.

Training and Support:

Users need to comprehend the capabilities and limitations of the system, requiring training to effectively utilize it.

System Sustainability

Continuous Learning and Updates:

The system must be capable of learning from and updating its knowledge based on the latest medical research to maintain accuracy and relevance.

Cost-Benefit Analysis:

The cost of implementing such a system needs to be compared to the potential medical benefits to assess its economic feasibility.

Overall, achieving a DIKWP processing system interfacing with GPT-4 is technically feasible, but it poses a series of challenges, including accuracy, data security, system integration, user experience, and sustainability. These challenges need to be gradually addressed during the actual development process, and extensive testing and evaluation are essential before implementation to ensure the system's efficacy and reliability.

Conclusion and Outlook

The DIKWP model provides a comprehensive treatment decision framework for both healthcare providers and patients. At every stage of gout treatment, from data collection to the achievement of treatment goals, the DIKWP model plays a crucial role. In the future, with the advancement of medical informatics, the DIKWP model will further aid the healthcare industry in achieving precision medicine, enhancing patient treatment outcomes, and improving quality of life.

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[34] Duan Y. DIKWP Digital Economics 12 Chain Machine Learning Chain: Data Learning, Information Learning, Knowledge Learning, Intelligent Learning, Intentional Learning. DOI: 10.13140/RG.2.2.26565.63201. https://www.researchgate.net/publication/374266062_DIKWP_shuzijingjixue_12_lianzhijiqixuexilian_shujuxuexi-xinxixuexi-zhishixuexi-zhihuixue_xi-yituxuexi_duanyucongYucong_Duan. 2023

[35] Duan Y. Big Data and Small Data Governance Based on DIKWP Model: Challenges and Opportunities for China. DOI: 10.13140/RG.2.2.21532.46724. https://www.researchgate.net/publication/374266054_jiyuDIKWPmoxingdedashujuyuxiaoshujuzhili_zhongguodetiaozhanyujiyu. 2023.

[36] Duan Y. DIKWP is based on digital governance: from "data governance", "information governance", "knowledge governance" to "wisdom governance". "Analysis of the current situation. DOI: 10.13140/RG.2.2.23210.18883. https://www.researchgate.net/publication/374265977_DIKWPjiyushuzizhilicongshujuzhilixinxizhilizhishizhilidaozhihuihuazhilidexianzhuangfenxi. 2023.

[37] Duan Y. Exploration of the nature of data tenure and rights enforcement issues based on the DIKWP model. DOI: 10.13140/RG.2.2.35793.10080. https://www.researchgate.net/publication/374265942_jiyu_DIKWP_moxingdeshujuquanshuxingzhiyuquequanwentitantao_duanyucongYucong_Duan. 2023.

[38] Duan Y. The DIKWP Model: Bridging Human and Artificial Consciousness. DOI: 10.13140/RG.2.2.23839.33447. https://www.researchgate.net/publication/374265912_DIKWP_moxingrenleiyurengongyishideqiaoliang_duanyucongYucong_Duan. 2023.

[39] Duan Y. An Exploration of Data Assetisation Based on the DIKWP Model: Definitions, Challenges and Prospects. DOI: 10.13140/RG.2.2.24887.91043. https://www.researchgate.net/publication/374265881_jiyu_DIKWP_moxingdeshujuzichanhuatanjiudingyitiaozhanyuqianjing_duanyucongYucong_Duan. 2023.

[40] Duan Y. Purpose-driven DIKWP Resource Transformation Processing: A New Dimension of Digital Governance. DOI: 10.13140/RG.2.2.29921.07529. https://www.researchgate.net/publication/374265796_yituqudongde_DIKWP_ziyuanzhuanhuachulishuzizhilidexinweidu_duanyucongYucong_Duan. 2023.

Duan Yucong, male, currently serves as a member of the Academic Committee of the School  of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial intelligence technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.

Distinction and Connection between Data and Information

Data is an objective description of the real world, representing records of facts such as numbers, text, and symbols. Data itself does not inherently carry direct significance, but it serves as the cornerstone for constructing information. From a mathematical perspective, data can be viewed as elements within a set, and these sets represent concrete representations of the "similar" semantics we comprehend. For instance, a set of temperature readings constitutes data, describing specific temperature values in a particular environment.

Information is the meaningful output of processed and organized data, representing expressions of "distinct" semantics within the data. In mathematical terms, information can be defined through the relationships and differences between data. For instance, the average, fluctuation range, or trend of temperature readings constitutes information, providing a cognitive understanding beyond mere data.

Formation and Application of Knowledge

Knowledge is the further refinement and understanding of information, achieved through connections, patterns, pattern recognition, and summarization of experiences, resulting in a profound insight into the world. In mathematical models, knowledge can be viewed as functions based on sets of information that can interpret the connections and principles behind the information. For instance, by analyzing temperature readings multiple times, we may deduce patterns in the temperature variations throughout the day, and this pattern constitutes knowledge.

The Nature and Application of Wisdom

Wisdom is the profound application of knowledge, considering aspects such as values, ethics, and morality. Wisdom is challenging to quantify mathematically, but it can be approximated through decision models. These models are based on knowledge and information while incorporating value judgments and anticipated goals. For example, wisdom might guide us to choose an appropriate air conditioning temperature to reduce energy consumption on a hot day.

The Significance and Processing of Purpose

Purpose, the ultimate output of the DIKWP model, represents our understanding (input) of specific phenomena and our goals (output). Mathematically, purpose can be modeled as a function or mapping, transforming the input DIKWP content into specific objectives or outcomes. For instance, in natural language processing, purpose recognition generates an output that satisfies user needs (such as performing a task or providing information) based on the user's input (instructions or queries).

The Objectivity and Subjective Refinement of Data

From an objective standpoint, data is a direct record of the real world, including raw numbers, text, and images obtained through observation. Subjective refinement of data involves the classification and interpretation of these raw records to impart specific meanings. For example, in meteorology, temperature and humidity readings are objective data, while associating these readings with specific weather phenomena is a form of subjective refinement.

Diversity and Semantic Reconstruction of Information

Information is the processing and organization of data, reflecting the diversity and distinctiveness inherent in the data. Objectively, information is a meaningful combination of data, such as the summarization of statistical data. On a subjective level, the semantic reconstruction of information involves further interpretation and understanding of these combinations, enabling us to identify and leverage the intrinsic value of the data.

Integrity and Wisdom Construction of Knowledge

Knowledge is a deeper understanding derived from the analysis, comparison, and reasoning based on information. Objectively, it manifests as a set of facts, principles, and patterns. From a subjective perspective, the intellectual construction of knowledge requires internalizing these facts and patterns into personal cognitive structures, guiding our behavior and decision-making.

Ethical Values and Judgment of Wisdom

Wisdom is the application and transformation of knowledge; it is not merely the accumulation of information and knowledge but, more importantly, the ability to use this knowledge to make insightful decisions. Objectively, wisdom is manifested as an efficient decision-making capability based on knowledge. On a subjective level, the ethical values and judgment of wisdom require us to consider morals and values in decision-making to achieve the maximum social and personal benefit.

Implementation of Purpose and Goal Orientation

Purpose is the ultimate goal of the DIKWP model, describing what we aim to achieve through the processing of information and knowledge. Objectively, purpose can be seen as a predetermined outcome or output. Subjectively, the implementation process of purpose requires us to apply wisdom in specific environments and situations to reach our goals.