ArticleJournal of the American Medical Informatics Association : JAMIA2023
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.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Characterization and validation of EHR computable phenotypes for Long COVID using patient-reported symptoms: insights from the nationwide RECOVER program.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Harmonization of Real-World Data for Vaccine-Preventable Infectious Diseases: Integration of SARS-CoV-2 Diagnostic and Serologic Data From Multiple Sources.Open forum infectious diseases · 2026Article
- Multi-scale data improves performance of machine learning model for long COVID identification.Communications medicine · 2026Article
- Leveraging the All of Us research program to advance heart, lung, blood, and sleep research.American journal of epidemiology · 2025Article
- Enhancing Machine Learning Explainability of Disaster Preparedness Models from the FEMA National Household Survey to Inform Tailored Population Health Interventions.Population health management · 2025Article
- Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: a machine learning modelling study from the N3C and RECOVER consortia.The Lancet. Digital health · 2025Article
- Identifying commonalities and differences between EHR representations of PASC and ME/CFS in the RECOVER EHR cohort.Communications medicine · 2025Article
- Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative.Journal of medical Internet research · 2025Article
- National COVID Cohort Collaborative data enhancements: a path for expanding common data models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025Article
- Returning value to communities from the All of Us Research Program through innovative approaches for data use, analysis, dissemination, and research capacity building.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Digital approaches in post-COVID healthcare: a systematic review of technological innovations in disease management.Biology methods & protocols · 2024Review
- AI in health: keeping the human in the loop.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.