Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

ABSTRACT:

Objective

Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database.

Design

Observational cohort study.

Setting

Five hospitals, from November 2008 until January 2013.

Patients

Hospitalized ward patients

Interventions

None

Measurements and main results

Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic

SUBMITTER: Churpek MM 

PROVIDER: S-EPMC4736499 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature