Dataset Information

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

ABSTRACT: Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic regression (dCLR) algorithm can effectively account for between-site heterogeneity and requires only one round of communication. Our simulation study and data application with the data of 14,215 COVID-19 patients from 230 clinical sites in the UnitedHealth Group Clinical Research Database demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when efficiently integrating data from multiple clinical sites. Our algorithm is therefore a practical alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

SUBMITTER: Tong J

PROVIDER: S-EPMC9198031 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Tong Jiayi J Luo Chongliang C Islam Md Nazmul MN Sheils Natalie E NE Buresh John J Edmondson Mackenzie M Merkel Peter A PA Lautenbach Ebbing E Duan Rui R Chen Yong Y

NPJ digital medicine 20220614 1

Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic ...[more]

PMID: 35701668

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Project description:MotivationIntegrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems.ResultsWe propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.Availability and implementationCode and data are available at https://github.com/GauravPandeyLab/ensemble_integration.Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Dataset Information

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Publications

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Similar Datasets

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