通过预测脏和肝功能恶化,个性化化疗患者的监测
Pinkie Chambers1,2, Matthew Watson3, John Bridgewater2,4
1UCL School of Pharmacy, London, UK.
Cancer medicine
|August 23, 2023
概括
机器学习可以准确地识别那些有脏和肝脏功能障碍风险的化疗患者. 这种模型通过对高风险患者进行更密切的监测并减少对低风险患者不必要的测试来提高安全性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 化疗可以导致和肝功能障碍,增加毒性风险.
- 监测器官功能对于化疗期间的患者安全至关重要.
- 需要有效的方法来识别需要更密切监测的患者.
研究的目的:
- 开发一种机器学习模型,以预测化疗患者的和肝功能障碍.
- 为了实现更安全,更有效的患者监测服务.
主要方法:
- 一个多层感知器 (MLP) 模型使用1214名患者的回顾性数据进行了训练.
- 该模型预测了胆红素和肌素水平不可接受的上升.
- 模型性能使用来自独立医院的数据进行了验证.
主要成果:
- 在MLP模型中,在训练套件上表现出高灵敏度和特异性 (>0.95).
- 验证显示了良好的灵敏度和高特异性来预测肌素和胆红素的变化.
- 在验证组中,曲线下的面积 (AUC) 值为0.76的肌素和0.72的 bilirubin.
结论:
- 多层感知子 (MLP) 模型可以有效地识别有器官功能障碍风险的化疗患者.
- 该模型可以减少低风险患者的血液检测频率.
- 它通过确保高风险患者获得必要的监测来提高安全性.
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