Can we develop real-world prognostic models using observational healthcare data? Large-scale experiment to investigate model sensitivity to database and phenotypes

These results highlight the importance of validating a model developed using observational data in the clinical setting prior to using it for decision-making. Calibration and Brier score should be evaluated to prevent miscalibrated risk estimates being used to aid clinical decisions.

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Publication Details

Journal
Diagnostic and Prognostic Research
Published
2025-04-17
DOI
https://doi.org/10.1186/s41512-025-00191-x
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
2.48
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article

Can we develop real-world prognostic models using observational healthcare data? Large-scale experiment to investigate model sensitivity to database and phenotypes

Jenna Reps, Peter R. Rijnbeek, Patrick B. Ryan
1 citations
Diagnostic and Prognostic Research
Machine Learning in Healthcare
2.48
article

Can we develop real-world prognostic models using observational healthcare data? Large-scale experiment to investigate model sensitivity to database and phenotypes

Jenna Reps, Peter R. Rijnbeek, Patrick B. Ryan
article en
1 citations

Abstract

These results highlight the importance of validating a model developed using observational data in the clinical setting prior to using it for decision-making. Calibration and Brier score should be evaluated to prevent miscalibrated risk estimates being used to aid clinical decisions.

Diagnostic and Prognostic ResearchVol. 9(1)
Erasmus MC (NL)
Openalex Percentile: Top 11%
Machine Learning in Healthcare
2.48
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