Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm.
Objectives: We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites.Materials and Methods: ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the fir...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 27; no. 3; pp. 376 - 386 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141752011&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141752011 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Mar2020 vid: 27 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 141752011 141752011 NLM31816040 10.1093/jamia/ocz199 NLM31816040 141752011 ppf: 376 ppct: 10 formats: tig: atl: Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm. aug: au: 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 affil: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA sug: subj: Privacy and Confidentiality Perinatal Death Etiology Adverse Drug Event Algorithms Logistic Regression Data Collection Computer Simulation Odds Ratio Female Pregnancy Scales Female ab: Objectives: We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites.Materials and Methods: 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 (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System. The estimation accuracy was evaluated by comparing it with the estimator based on the combined individual participant data or pooled data (ie, gold standard).Results: Our simulation studies revealed that the relative estimation bias of ODAL1 compared with the pooled estimates was <3%, and the ratio of standard errors was <1.25 for all scenarios. ODAL2 achieved higher accuracy (with relative bias <0.1% and ratio of standard errors <1.05). In real data analysis, we investigated the associations of 100 medications with fetal loss during pregnancy. We found that ODAL1 provided estimates with relative bias <10% for 85% of medications, and ODAL2 has relative bias <10% for 99% of medications. For communication cost, ODAL1 requires transferring p numbers from each site to the local site and ODAL2 requires transferring (p×p+p) numbers from each site to the local site, where p is the number of parameters in the regression model.Conclusions: This study demonstrates that ODAL is privacy-preserving and communication-efficient with small bias and high statistical efficiency. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|