Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm

We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites. ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order...

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Vydané v:	Journal of the American Medical Informatics Association : JAMIA Ročník 27; číslo 3; s. 376
Hlavní autori:	Duan, Rui, Boland, Mary Regina, Liu, Zixuan, Liu, Yue, Chang, Howard H, Xu, Hua, Chu, Haitao, Schmid, Christopher H, Forrest, Christopher B, Holmes, John H, Schuemie, Martijn J, Berlin, Jesse A, Moore, Jason H, Chen, Yong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England 01.03.2020
Predmet:	Algorithms Computer Simulation Confidentiality Data Analysis Datasets as Topic Drug-Related Side Effects and Adverse Reactions Electronic Health Records Female Fetal Death - etiology Humans Logistic Models Odds Ratio Pregnancy distributed algorithm learning health system logistic regression electronic health record
ISSN:	1527-974X, 1527-974X
On-line prístup:	Zistit podrobnosti o prístupe
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