Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations.
第一作者:
Emily J,MacKay
第一单位:
Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.;Penn Center for Perioperative Outcomes Research and Transformation (CPORT), University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.;Penn's Cardiovascular Outcomes, Quality and Evaluative Research Center (CAVOQER), University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
作者:
医学主题词
曲线下面积(Area Under Curve);数据库, 事实型(Databases, Factual);住院(Hospitalization);人类(Humans);Logistic模型(Logistic Models);Medicare(Medicare);模型, 理论(Models, Theoretical);死亡率(Mortality);ROC曲线(ROC Curve);回顾性研究(Retrospective Studies);危险性评估(Risk Assessment);治疗结果(Treatment Outcome);美国(United States)
DOI
10.1371/journal.pone.0252585
PMID
34081720
发布时间
2021-11-17
- 浏览0
PloS one
e0252585页
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