Calibration drift in regression and machine learning models for acute kidney injury | Zendy

Sharon E. Davis | Zendy; Thomas A. Lasko | Zendy; Guanhua Chen | Zendy; Edward D. Siew | Zendy; Michael E. Matheny | Zendy

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Calibration drift in regression and machine learning models for acute kidney injury

Author(s) -

Sharon E. Davis,

Thomas A. Lasko,

Guanhua Chen,

Edward D. Siew,

Michael E. Matheny

Publication year - 2017

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocx030

Subject(s) - calibration , random forest , regression , computer science , machine learning , regression analysis , confidence interval , predictive modelling , prediction interval , population , artificial intelligence , statistics , linear regression , medicine , mathematics , environmental health

Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population.

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