Work

Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.

Year: 2017

Type: article

Abstract: Advanced regression and machine learning models can provide personalized risk predictions to support clinical decision-making. We aimed to understand whether modeling methods impact the tendency of calibration to deteriorate as patient populations shift over time, with the goal of informing model updating practices. We developed models for 30-day hospital mortality using seven common regression and machine learning methods. Models were developed on 2006 admissions to Department of Veterans Affairs hospitals and validated on admissions in 2007-2013. All models maintained discrimination. Calibration was stable for the neural network model and declined for all other models. The L-2 penalized logistic regression and random forest models experienced smaller magnitudes of calibration drift than the other regression models. Calibration drift was linked with a changing case mix rather than shifts in predictoroutcome associations or outcome rate. Model updating protocols will need to be tailored to variations in calibration drift across methods.

Source: PubMed Central

Authors: Sharon E Davis, Thomas A Lasko, Guanhua Chen, Michael E Matheny

Institutions: Vanderbilt University, VA Tennessee Valley Healthcare System

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, U.S. National Library of Medicine

Awards: Scalable Biomedical Pattern Recognition Via Deep Learning, Vanderbilt Biomedical Informatics Training Program