Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

ABSTRACT:

Objectives

The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.

Design

Retrospective, population-based registry study.

Setting

Swedish health services.

Primary and secondary outcome measures

All cause 30-day mortality.

Methods

Electronic health records (EHRs) and administrative data were used to train six supervised machine learning models to predict all-cause mortality within 30 days in patients discharged from EDs in southern Sweden, Europe.

Participants

The models were trained using 65 776 ED visits and validated on 55 164 visits from a separate ED to which the mode

SUBMITTER: Blom MC 

PROVIDER: S-EPMC6701621 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature