使用贝叶斯网络来预测接受非小细胞肺癌全身治疗的患者的紧急护理访问
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
概括
整合患者报告结果和可穿戴传感器数据的机器学习模型显著改善了对接受全身治疗的非小细胞肺癌患者紧急护理访问的预测. 这种方法可以提高癌症护理质量和患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 数字健康数字健康
- 机器学习 机器学习
背景情况:
- 针对非小细胞肺癌 (NSCLC) 的全身疗法 (ST) 可能导致影响患者结果的毒性.
- 预测和管理与治疗相关的毒性对于改善患者护理和减少医疗保健利用是至关重要的.
研究的目的:
- 为了前性地收集患者报告结果 (PRO) 数据,可穿戴传感器数据 (WSD) 和临床数据.
- 开发一种机器学习 (ML) 算法,用于预测接受ST的NSCLC患者的紧急护理 (UC) 访问.
主要方法:
- 患有NSCLC的患者在ST期间 (第0-60天) 完成了PROMIS-57并佩戴Fitbit.
- 从医疗记录中收集了人口统计和临床数据.
- 开发了可解释贝叶斯网络 (BN) 模型来预测UC访问.
主要成果:
- 使用人口统计和临床数据的初始BN模型显示UC访问的预测准确度中等 (AUC 0.72-0.81).
- 整合PRO和WSD显著提高了模型性能 (最终AUC为0.86,P<.001).
- 该研究包括58名NSCLC患者,平均年龄为69岁.
结论:
- 多维数据源 (人口统计,临床,PRO,WSD) 改善了医疗保健利用的ML预测模型.
- 可解释的ML可以预测并潜在地预防治疗毒性和医疗保健利用.
- 这种方法可以提高患者的治疗结果和癌症护理的质量.
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