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Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
Bret Nestor, Matthew B. A. McDermott, Willie Boag +5
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 ca…
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan +3
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasona…
Generalizability of predictive models for intensive care unit patients
Alistair E. W. Johnson, Tom J. Pollard, Tristan Naumann
A large volume of research has considered the creation of predictive models for clinical data; however, much existing literature reports results using only a single source of data.…
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan +4
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operat…
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones +15
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held…