Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data.

ABSTRACT:

Purpose

The objective of the current study was to develop and test the performances of different ML algorithms which were trained using patient-reported symptom severity data to predict mortality within 180 days for patients with advanced cancer.

Methods

We randomly selected 630 of 689 patients with advanced cancer at our institution who completed symptom PRO measures as part of routine care between 2009 and 2020. Using clinical, demographic, and PRO data, we trained and tested four ML algorithms: generalized regression with elastic net regularization (GLM), extreme gradient boosting (XGBoost) trees, support vector machines (SVM), and a single hidden layer neural network (NNET). We assessed the performance of algorithms individually as well as part of an unweighted voting en

SUBMITTER: Xu C 

PROVIDER: S-EPMC9992030 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature