Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

Nestor Bret
Nestor Bret
McDermott Matthew B. A.
McDermott Matthew B. A.
Boag Willie
Boag Willie
Berner Gabriela
Berner Gabriela
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Abstract:

When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve. Due to de-identification requirements, however, current experimental practices for public ...More

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