Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.
AMIA Annu Symp Proc
; 2017: 625-634, 2017.
Article
em En
| MEDLINE
| ID: mdl-29854127
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Redes Neurais de Computação
/
Mortalidade Hospitalar
/
Aprendizado de Máquina
Tipo de estudo:
Etiology_studies
/
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Female
/
Humans
/
Male
País/Região como assunto:
America do norte
Idioma:
En
Revista:
AMIA Annu Symp Proc
Ano de publicação:
2017
Tipo de documento:
Article