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

Rui Duan, Mary Regina Boland, Zixuan Liu, Yue Liu, Howard H. Chang, Hua Xu, Haitao Chu, Christopher H. Schmid, Christopher B. Forrest, John H. Holmes, Martijn J. Schuemie, Jesse A. Berlin, Jason H. Moore, Yong Chen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)376-385
Number of pages10
JournalJournal of the American Medical Informatics Association
Volume27
Issue number3
DOIs
StatePublished - Mar 1 2020

Bibliographical note

Funding Information:
This work is supported in part by National Institutes of Health grants 1R01LM012607 (RD and YC), 1R01AI130460 (RD and YC), P50MH113840 (YC), 1R01AI116794 (JHM and YC) and R01LM009012 (JHM and YC).

Keywords

  • distributed algorithm
  • electronic health record
  • learning health system
  • logistic regression

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