@文章{信息:doi/10.2196/16213，作者="彭丽宁、萧丽宁、李飞元、黄伟菊、陈良功"，标题="多病脆弱性指数的假设驱动方法与数据驱动方法的比较:一种机器学习方法"，期刊="J Med Internet Res"，年="2020"，月="Jun"，日="11"，卷="22"，数="6"，页数="e16213"，关键词="多病脆弱性指数;机器学习;随机森林;意外住院;重症监护病房入院;背景:利用大数据和累积缺陷理论建立多病脆弱指数(mFI)已成为公共卫生和卫生保健服务领域普遍接受的方法。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)。 Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. 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. ", issn="1438-8871", doi="10.2196/16213", url="//www.mybigtv.com/2020/6/e16213/", url="https://doi.org/10.2196/16213", url="http://www.ncbi.nlm.nih.gov/pubmed/32525481" }