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

Abstract

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. 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 UC visits. Patients in the training data set (N = 58) were age 69 years on average (range, 35-89) and mostly female (57%), White (88%), and non-Hispanic (95%) patients with adenocarcinoma (69%). Initial BN models trained on demographic and clinical data demonstrated moderate predictive accuracy on cross-validation for UC visits before ST (AUC, 0.72 [95% CI, 0.57 to 0.80]) and during ST (AUC, 0.81 [95% CI, 0.63 to 0.89]). Incorporating PRO and WSD during ST yielded enhanced models with significantly improved performance (final AUC, 0.86 [95% CI, 0.76 to 0.95]) via DeLong test (<i>P</i> < .001). Multidimensional data sources, including demographic, clinical, PRO, and WSD, can enhance ML predictive models to elucidate complex, interactive factors influencing health care utilization during the first 60 days of ST. Use of explainable ML to predict and prevent treatment toxicities and health care utilization could improve patient outcomes and enhance the quality of cancer care delivery.