De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository

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Creator
  • Pfaff, Emily R
    • Affiliation: School of Medicine, Department of Medicine
  • Girvin, Andrew T
    • Other Affiliation: Palantir Technologies
  • Crosskey, Miles
    • Other Affiliation: CoVar Applied Technologies
  • Gangireddy, Srushti
    • Other Affiliation: Vanderbilt University Medical Center
  • Master, Hiral
    • Other Affiliation: Vanderbilt University Medical Center
  • Wei, Wei-Qi
    • Other Affiliation: Vanderbilt University Medical Center
  • Kerchberger, V Eric
    • Other Affiliation: Vanderbilt University Medical Center
  • Weiner, Mark
    • Other Affiliation: Weill Cornell Medicine
  • Harris, Paul A
    • Other Affiliation: Vanderbilt University Medical Center
  • Basford, Melissa
    • Other Affiliation: Vanderbilt University Medical Center
  • Lunt, Chris
    • Other Affiliation: National Institutes of Health
  • Chute, Christopher G
    • Other Affiliation: Johns Hopkins Schools of Medicine
  • Moffitt, Richard A
    • Other Affiliation: Emory University
  • Haendel, Melissa
    • Other Affiliation: University of Colorado Anschutz Medical Campus
  • N3C and RECOVER Consortia
Abstract
  • Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH’s All of Us study partnered to reproduce the output of N3C’s trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.
Date of publication
DOI
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Resource type
  • Article
Journal title
  • Journal of the American Medical Informatics Association
Journal volume
  • 30
Journal issue
  • 7
Page start
  • 1305-1312
Language
  • English
ISSN
  • 1527-974X
Publisher
  • Oxford University Press

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