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Related Experiment Video

Updated: Jan 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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

Brian D Gonzalez¹, Xiaoyin Li¹, Lisa M Gudenkauf¹

¹Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL.

JCO Clinical Cancer Informatics

|September 12, 2025

Summary

Machine learning models integrating patient-reported outcomes and wearable sensor data significantly improved prediction of urgent care visits for non-small cell lung cancer patients undergoing systemic therapy. This approach can enhance cancer care quality and patient outcomes.

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Published on: October 3, 2025

Area of Science:

Oncology
Digital Health
Machine Learning

Background:

Systemic therapy (ST) for non-small cell lung cancer (NSCLC) can lead to toxicities impacting patient outcomes.
Predicting and managing treatment-related toxicities is crucial for improving patient care and reducing healthcare utilization.

Purpose of the Study:

To prospectively collect patient-reported outcome (PRO) data, wearable sensor data (WSD), and clinical data.
To develop a machine learning (ML) algorithm to predict urgent care (UC) visits in NSCLC patients receiving ST.

Main Methods:

Patients with NSCLC completed PROMIS-57 and wore a Fitbit during ST (days 0-60).
Demographic and clinical data were collected from medical records.
Explainable Bayesian Network (BN) models were developed to predict UC visits.

Main Results:

Initial BN models using demographic and clinical data showed moderate predictive accuracy for UC visits (AUC 0.72-0.81).
Incorporating PRO and WSD significantly enhanced model performance (final AUC 0.86, P < .001).
The study included 58 NSCLC patients, with a mean age of 69.

Conclusions:

Multidimensional data sources (demographic, clinical, PRO, WSD) improve ML predictive models for healthcare utilization.
Explainable ML can predict and potentially prevent treatment toxicities and healthcare utilization.
This approach can enhance patient outcomes and the quality of cancer care delivery.