%0期刊文章%@ 1438- 8871% I JMIR出版物%V 22卡塔尔世界杯8强波胆分析% N 6% P e16213% T多病脆弱指数假设驱动方法和数据驱动方法的比较:机器学习方法%彭a，李宁%肖飞元%李伟菊%黄世松%陈良功%+老年病学中心，台北退伍军人总医院，石排路二段201，台北市，11217,886 2 28757830，lkchen2@vghtpe.gov.tw %K多病脆弱指数%K机器学习%K随机森林%K非计划住院%K重症监护病房入院%K死亡率%D 2020 %7 11.6.2020 %9原始论文%J J医学互联网Res %G英文%X背景:利用大数据和累积赤字理论开发多病脆弱指数(mFI)已成为公共卫生和卫生保健服务中被广泛接受的方法。然而，在临床实践中，使用最关键的决定因素构建mFI和用剂量-反应关系对不同的风险组进行分层仍然是主要的挑战。目的:本研究旨在利用基于模型最优适应度选择变量的机器学习方法来开发mFI。此外，我们的目标是使用机器学习方法进一步建立4个风险实体，以实现各组之间的最佳区分，并演示剂量-反应关系。方法:在本研究中，我们使用台湾国家健康保险研究数据库，利用个体老年人的累积疾病/缺陷理论，开发了机器学习多病脆弱指数(ML-mFI)。与传统的mFI(疾病/缺陷的选择基于专家意见)相比，我们采用随机森林方法来选择预测老年人不良结果的最具影响力的疾病/缺陷。为确保生存曲线在随访过程中呈剂量-反应关系且重叠，我们制定了距离指数和覆盖指数，可在任何时间点将所有受试者的ML-mFI分为适合、轻度脆弱、中度脆弱和重度脆弱。进行生存分析以评估ML-mFI预测不良结局的能力，如非计划住院、重症监护病房(ICU)入院和死亡率。 Results: The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. Conclusions: The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society. %M 32525481 %R 10.2196/16213 %U //www.mybigtv.com/2020/6/e16213/ %U https://doi.org/10.2196/16213 %U http://www.ncbi.nlm.nih.gov/pubmed/32525481