Using machine learning to predict individual patient toxicities from cancer treatments.

ABSTRACT:

Purpose

Machine learning (ML) is a powerful tool for interrogating datasets and learning relationships between multiple variables. We utilized a ML model to identify those early breast cancer (EBC) patients at highest risk of developing severe vasomotor symptoms (VMS).

Methods

A gradient boosted decision model utilizing cross-sectional survey data from 360 EBC patients was created. Seventeen patient- and treatment-specific variables were considered in the model. The outcome variable was based on the Hot Flush Night Sweats (HFNS) Problem Rating Score, and individual scores were dichotomized around the median to indicate individuals with high and low problem scores. Model accuracy was assessed using the area under the receiver operating curve, and conditional partial dependenc

SUBMITTER: Cole KM 

PROVIDER: S-EPMC9385785 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature