Dynamic prediction of hospital admission with medical claim data.

ABSTRACT:

Background

Congestive heart failure is one of the most common reasons those aged 65 and over are hospitalized in the United States, which has caused a considerable economic burden. The precise prediction of hospitalization caused by congestive heart failure in the near future could prevent possible hospitalization, optimize the medical resources, and better meet the healthcare needs of patients.

Methods

To fully utilize the monthly-updated claim feed data released by The Centers for Medicare and Medicaid Services (CMS), we present a dynamic random survival forest model adapted for periodically updated data to predict the risk of adverse events. We apply our model to dynamically predict the risk of hospital admission among patients with congestive heart failure identified usi

SUBMITTER: Yang T 

PROVIDER: S-EPMC6354329 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature