Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer.

ABSTRACT:

Importance

Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices.

Objective

To validate an electronic health record-embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort.

Design, setting, and participants

This prognostic study comprised a prospective cohort of patients with outpatient oncology encounters between March 1, 2019, and April 30, 2019. An ML algorithm, trained on retrospective data from a subset of practices, predicted 180-day mortality risk between 4 and 8 days b

SUBMITTER: Manz CR 

PROVIDER: S-EPMC7516810 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature