心血管病风险分析[Appl. Sci.专栏第八篇发表论文]

已有 1043 次阅读 2023-1-16 07:35 |系统分类:论文交流

我在Applied Sciences(综合性、交叉性期刊,CiteScore=3.70IF=2.84)组织了一个Special Issue,大题目是“大数据分析进展”,比较宽泛。该专栏的推出主要是为了回应因为可获取数据和数据分析的平台、工具的快速增长给自然科学和社会科学带来的重大影响。我们特别欢迎(但不限于)下面四类稿件:(1)数据分析中的基础理论分析,例如一个系统的可预测性(比如时间序列的可预测性)、分类问题的最小误差分析、各种数据挖掘结果的稳定性和可信度分析;(2)数据分析的新方法,例如挖掘因果关系的新方法(这和Topic 1也是相关的)、多模态分析的新方法、隐私计算的新方法等等;(3)推出新的、高价值的数据集、数据分析平台、数据分析工具等等;(4)把大数据分析的方法用到自然科学和社会科学的各个分支(并获得洞见),我们特别喜欢用到那些原来定量化程度不高的学科。




A Cardiovascular Disease Risk Score Model Based on High Contribution Characteristics


Cardiovascular disease (CVD) risk prediction shows great significance for disease diagnosis and treatment, especially early intervention for CVD, which has a direct impact on preventing and reducing adverse outcomes. In this paper, we collected clinical indicators and outcomes of 14,832 patients with cardiovascular disease in Shanxi, China, and proposed a cardiovascular disease risk prediction model, XGBH, based on key contributing characteristics to perform risk scoring of patients’ clinical outcomes. The XGBH risk prediction model had high accuracy, with a significant improvement compared to the baseline risk score (AUC = 0.80 vs. AUC = 0.65). At the same time, we found that with the addition of conventional biometric variables, the accuracy of the model’s CVD risk prediction would also be improved. Finally, we designed a simpler model to quantify disease risk based on only three questions answered by the patient, with only a modest reduction in accuracy (AUC = 0.79), and providing a valid risk assessment for CVD. Overall, our models may allow early-stage intervention in high-risk patients, as well as a cost-effective screening approach. Further prospective studies and studies in other populations are needed to assess the actual clinical effect of XGBH risk prediction models.


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2 许培扬 杨正瓴

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