An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes

An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes

Abstract Clinical research networks (CRNs), made up of multiple healthcare systems each with patient data from several care sites, are beneficial for studying rare outcomes and increasing generalizability of results. While CRNs encourage sharing aggregate data across healthcare systems, individual s...

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Bibliographic Details
Main Authors: Mackenzie J. Edmondson, Chongliang Luo, Rui Duan, Mitchell Maltenfort, Zhaoyi Chen, Kenneth Locke, Justine Shults, Jiang Bian, Patrick B. Ryan, Christopher B. Forrest, Yong Chen
Format: Article
Language:English
Published: Nature Portfolio 2021-10-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-99078-2

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https://doi.org/10.1038/s41598-021-99078-2

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