ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Generalization of Machine Learning Approaches to Identify Notifiable Conditions from a Statewide Health Information Exchange.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Computational methods applied to syphilis: where are we, and where are we going?Frontiers in public health · 2023Pooled it
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- How data drift impacts the safety and interpretability of machine learning models predicting risk from blood glucose control.BMJ digital health & AI · 2026Article
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- Adaptable graph neural networks design to support generalizability for clinical event prediction.Journal of biomedical informatics · 2025Article
- Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification.Diagnostics (Basel, Switzerland) · 2025Article
- The Role of Machine Learning Models in Predicting Cirrhosis Mortality: A Systematic Review.Cureus · 2025Review
- Empirical data drift detection experiments on real-world medical imaging data.Nature communications · 2024Article
- Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.Healthcare (Basel, Switzerland) · 2023Review
- A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler.Annals of neurology · 2023Article
- Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?JMIR AI · 2023Article
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- Conditional generation of medical time series for extrapolation to underrepresented populations.PLOS digital health · 2022Article
- An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology.Diagnostics (Basel, Switzerland) · 2022Review
- Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.Scientific reports · 2021Article
- Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.Pathogens (Basel, Switzerland) · 2021Review
- Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.Journal of medical Internet research · 2021Article
- Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: AnFrontiers in bioengineering and biotechnology · 2021Article
- More Generalizable Models For Sepsis Detection Under Covariate Shift.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare analytics is impeded by a lack of machine learning (ML) model generalizability, the ability of a model to predict accurately on varied data sources not included in the model's training dataset. We leveraged free-text laboratory data from a Health Information Exchange network to evaluate ML generalization using Notifiable Condition Detection (NCD) for public health surveillance as a use case. We 1) built ML models for detecting syphilis, salmonella, and histoplasmosis; 2) evaluated generalizability of these models across data from holdout lab systems, and; 3) explored factors that influence weak model generalizability. Models for predicting each disease reported considerable accuracy. However, they demonstrated poor generalizability across data from holdout lab systems being tested. Our evaluation determined that weak generalization was influenced by variant syntactic nature of free-text datasets across each lab system. Results highlight the need for actionable methodology to generalize ML solutions for healthcare analytics.
Indexed as
Identifiers
32477634PMC7233074W3028902405What OpenQuestion holds
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.