Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study.

ABSTRACT: BACKGROUND:Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and risk adjustment for research. OBJECTIVE:Determine if the final set of demographic, vital sign, and laboratory data from a hospitalization can be used to accurately quantify 1-year mortality risk. DESIGN:A retrospective study using electronic medical record data linked with the state death registry. PARTICIPANTS:A total of 59,848 hospitalized patients within a six-hospital network over a 4-year period. MAIN MEASURES:The last set of vital signs, complete blood count, basic and complete metabolic panel, demographic information, and ICD codes. The outc

SUBMITTER: Sahni N 

PROVIDER: S-EPMC5975145 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature