Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations.

ABSTRACT:

Objective

This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations.

Methods

This retrospective, observational cohort study, used a random 5% sample of 770,777 fee-for-service Medicare beneficiaries with an inpatient hospitalization between 2009-2011. The machine learning algorithms tested included: support vector machine, random forest, multilayer perceptron, extreme gradient boosted tree, and logistic regression. The extreme gradient boosted tree algorithm outperformed the alternatives and was the machine learning method used for the final risk model. Primary outcome was 30-day mortality. Secondary outcomes were: rehospitalization, and any of 23 adverse clinical even

SUBMITTER: MacKay EJ 

PROVIDER: S-EPMC8174683 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature