药物护理记录中使用患者关切的不良事件信号检测:深度学习模型的评估
Satoshi Nishioka1, Satoshi Watabe1, Yuki Yanagisawa1
1Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.
Journal of medical Internet research
|April 16, 2024
概括
深度学习模型准确地选了患者叙述中的不良事件信号,提高了抗癌治疗的安全性. 这些模型可以识别需要医疗干预的症状,帮助实时监测患者.
科学领域:
- 在瘤学瘤学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
背景情况:
- 早期发现和管理不良事件对于改善抗癌治疗结果至关重要.
- 患者生成的文本数据和深度学习为与安全有关的事件评估提供了新的途径.
- 对个别患者的实时安全监测和对不良事件信号的深度学习模型的验证尚未得到充分研究.
研究的目的:
- 评估深度学习模型在查需要医疗保健专业人员干预的临床显著不良事件信号方面的有效性.
- 评估这些深度学习模型对来自社区药房的患者关注数据的适用性.
主要方法:
- 深度学习模型应用于癌症患者从药物护理 (SOAP格式) 获得的主观 (S) 记录.
- 评估包括评估提取的不良事件信号与医疗行动和处方抗癌药物的对比.
- 数据包括来自2,479名接受抗癌药物治疗的患者的30,784个主观记录.
主要成果:
- 深度学习模型在检测手脚综合征 (91%) 和限制日常生活的不良事件 (80.1%) 中取得了超过80%的准确性.
- 模型成功选了需要医疗干预的信号,反映了抗癌药物的副作用.
- 常见的症状包括疼痛/麻木 (36.3%),发烧 (29.3%) 和恶心 (25.5%).
结论:
- 深度学习模型有效地选出需要干预的临床重要不良事件信号.
- 这些模型证明了在制药护理记录中对主观患者信息的适用性.
- 这种方法支持实时安全监测,并加强在抗癌治疗期间的患者护理.
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