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

IF 2.8 Q2 ONCOLOGY
JCO Clinical Cancer Informatics Pub Date : 2025-09-01 Epub Date: 2025-09-12 DOI:10.1200/CCI-24-00315
Brian D Gonzalez, Xiaoyin Li, Lisa M Gudenkauf, Jerrin J Pullukkara, Laura B Oswald, Aasha I Hoogland, Trung Le, Issam El Naqa, Andreas N Saltos, Eric B Haura, Yi Luo
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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 UC visits.

Results: 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 (P < .001).

Conclusion: 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.

使用贝叶斯网络预测非小细胞肺癌患者接受全身治疗的紧急护理就诊。
目的:接受全身治疗(ST)的非小细胞肺癌(NSCLC)患者会经历对患者预后产生负面影响的毒性。本研究旨在测试一种前瞻性收集患者报告结果(PRO)数据、可穿戴传感器数据(WSD)和临床数据的方法,并开发一种机器学习(ML)算法来预测医疗保健利用,特别是紧急护理(UC)就诊。材料和方法:NSCLC患者完成了promise -57 PRO生活质量测量,并佩戴Fitbit来监测从ST开始到第60天患者产生的健康数据。人口统计学和临床资料从病历中提取。基于贝叶斯网络(BNs)的ML可解释模型用于开发UC访问的预测模型。结果:训练数据集中的患者(N = 58)平均年龄为69岁(范围为35-89),大多数为女性(57%),白人(88%)和非西班牙裔(95%)腺癌患者(69%)。根据人口统计学和临床数据训练的初始BN模型在交叉验证中对ST前(AUC, 0.72 [95% CI, 0.57至0.80])和ST期间(AUC, 0.81 [95% CI, 0.63至0.89])UC就诊的预测准确性中等。通过DeLong检验(P < 0.001),在ST期间加入PRO和WSD产生了性能显著提高的增强模型(最终AUC, 0.86 [95% CI, 0.76至0.95])。结论:包括人口统计学、临床、PRO和WSD在内的多维数据源可以增强ML预测模型,以阐明影响st前60天医疗保健利用的复杂、交互因素。使用可解释的ML预测和预防治疗毒性和医疗保健利用可以改善患者的预后,提高癌症护理的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
4.80%
发文量
190
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