Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer.

ABSTRACT:

Purpose

Patients receiving systemic therapy (ST) for non-small cell lung cancer (NSCLC) experience toxicities that negatively affect patient outcomes. This study aimed to test an approach for prospectively collecting patient-reported outcome (PRO) data, wearable sensor data (WSD), and clinical data, and develop a machine learning (ML) algorithm to predict health care utilization, specifically urgent care (UC) visits.

Materials and methods

Patients with NSCLC completed the PROMIS-57 PRO quality-of-life measure and wore a Fitbit to monitor patient-generated health data from ST initiation through day 60. Demographic and clinical data were abstracted from the medical record. ML explainable models on the basis of Bayesian Networks (BNs) were used to develop predictive models for

SUBMITTER: Gonzalez BD 

PROVIDER: S-EPMC12483286 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature