Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models

This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training da...

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Bibliographic Details
Published in:Scientific reports 2023-05, Vol.13 (1), p.8363-8363, Article 8363
Main Authors: Schinkel, Michiel, Bennis, Frank C., Boerman, Anneroos W., Wiersinga, W. Joost, Nanayakkara, Prabath W. B.
Format: Article
Language:English
Subjects:
631/114
631/114/1314
631/114/2397
631/114/2413
Artificial intelligence
Calibration
Datasets
Emergency medical care
Heterogeneity
Hospitals
Humanities and Social Sciences
Humans
Internal medicine
Machine Learning
Medicine
multidisciplinary
Prediction models
Science
Science (multidisciplinary)
Sepsis
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Description
Summary:This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training data but from just a single cohort. Although this concept seems simple and obvious, no current prediction model development guidelines recommend such an approach.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-35557-y