research-article

Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomes

Authors:

Christian Haudenschild,

Joshua LevyAuthors Info & Claims

SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

Pages 627 - 635

https://doi.org/10.1145/3477314.3507007

Published: 06 May 2022 Publication History

Abstract

Vast quantities of electronic patient medical data are currently being collated and processed in large federated data repositories. For instance, TriNetX, Inc., a global health research network, has access to more than 300 million patients, sourced from healthcare organizations, biopharmaceutical companies, and contract research organizations. As such, pipelines that are able to algorithmically extract huge quantities of patient data from multiple modalities present opportunities to leverage machine learning and deep learning approaches with the possibility of generating actionable insight. In this work, we present a modular, semi-automated end-to-end machine and deep learning pipeline designed to interface with a federated network of structured patient data. This proof-of-concept pipeline is disease-agnostic, scalable, and requires little domain expertise and manual feature engineering in order to quickly produce results for the case of a user-defined binary outcome event. We demonstrate the pipeline's efficacy with three different disease workflows, with high discriminatory power achieved in all cases.

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Cited By

Murthy CShri M(2024)Novel Architecture Integrating XAI with Blockchain and IoT Devices for Healthcare2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493418(1-5)Online publication date: 22-Feb-2024
https://doi.org/10.1109/ic-ETITE58242.2024.10493418
Levy JLu YMontivero MRamwala OMcFadden JMiles CDiamond AReddy RReddy RHudson TAzher ZPamal AGabbita SCronin TOuld Ismail AGoel TJacob SSuvarna AKim TZhang EReddy NRatna SZavras JVaickus L(2022)Artificial Intelligence, Bioinformatics, and PathologyAdvances in Molecular Pathology10.1016/j.yamp.2023.01.0025:1(e25-e52)Online publication date: Nov-2022
https://doi.org/10.1016/j.yamp.2023.01.002

Index Terms

Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomes
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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cover image ACM Conferences

SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

April 2022

2099 pages

ISBN:9781450387132

DOI:10.1145/3477314

Conference Chairs:
Jiman Hong
Soongsil University
,
Miroslav Bures
Czech Technical University, Czechia
,
Program Chairs:
Juw Won Park
University of Louisville
,
Tomas Cerny
Baylor University

Copyright © 2022 ACM.

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Published: 06 May 2022

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April 25 - 29, 2022

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Cited By

Murthy CShri M(2024)Novel Architecture Integrating XAI with Blockchain and IoT Devices for Healthcare2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493418(1-5)Online publication date: 22-Feb-2024
https://doi.org/10.1109/ic-ETITE58242.2024.10493418
Levy JLu YMontivero MRamwala OMcFadden JMiles CDiamond AReddy RReddy RHudson TAzher ZPamal AGabbita SCronin TOuld Ismail AGoel TJacob SSuvarna AKim TZhang EReddy NRatna SZavras JVaickus L(2022)Artificial Intelligence, Bioinformatics, and PathologyAdvances in Molecular Pathology10.1016/j.yamp.2023.01.0025:1(e25-e52)Online publication date: Nov-2022
https://doi.org/10.1016/j.yamp.2023.01.002

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