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首页> 外文期刊>BMC Medical Informatics and Decision Making >Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies
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Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

机译：电子患者报告的结果和机器学习预测免疫检查点抑制剂疗法的免疫相关不良事件

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Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the?severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. The?current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a?high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients.
机译：免疫检查点抑制剂（ICIS）引入了新的免疫相关不良事件（IRAE），来自各种器官系统而没有强烈的及时依赖治疗剂量。早期检测伊拉克人可能导致毒性概况和生活质量改善。电子（e）患者报告的结果（Pro）收集的症状数据可用作基于机器学习（ML）的预测模型的输入，用于早期检测IRAES。使用的数据集由两个数据源组成。第一个数据集由820名ICI治疗的晚期癌症患者的820次完成的症状问卷组成，包括使用Kaiku Healtal Digital Platform收集的18名监测症状。第二个数据集包括前瞻性地收集的IRAE数据，常见的术语标准，不良事件（CTCAE）类，26个伊拉克的严重程度。 ML模型采用极端梯度升压算法建立。第一个模型训练以检测伊拉斯的爆发的存在和第二个。培训的模型预测IRAE的存在具有优异的性能，基于四个度量：精度得分0.97，曲线下的面积（AUC）值0.99，F1 - 得分0.94和Matthew的相关系数（MCC）0.92。 IRAE发作的预测更困难，精度得分为0.96，AUC值0.93，F1分数0.66和MCC 0.64，但模型性能仍处于良好水平。当前的研究表明，基于ML的预测模型，使用EPRO数据作为输入，可以预测IRAE的存在和开始具有？高精度，表明使用ML算法的EPRO随访可以促进ICI的检测治疗的癌症患者。

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