从临床叙述中提取的不良事件的基于自然语言处理的可视化框架:朝着提高临床解释性
Masami Tsuchiya1, Yoshimasa Kawazoe2, Kiminori Shimamoto2
1Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo 105-8512, Japan.
Biological & pharmaceutical bulletin
|March 11, 2026
概括
这项研究引入了一种使用自然语言处理 (NLP) 和可视化来追踪主观不良事件 (AE) 的新框架,例如电子健康记录 (EHR) 中的疼痛. 这些发现突显了帕克利塔塞尔的价值.
科学领域:
- 临床信息学是一种临床信息学.
- 生物医学数据科学是生物医学数据科学.
- 药物监督 药物监督 药物监督
背景情况:
- 主观不良事件 (AE) 在结构化电子健康记录 (EHR) 数据中往往未被充分识别.
- 自然语言处理 (NLP) 可以从叙事EHR中提取AEs,但时间分析仍然具有挑战性.
- 可视化技术可以提高文本衍生AE数据的解释性.
研究的目的:
- 证明基于NLP的AE提取与主观症状的时间序列可视化相结合的框架的临床实用性.
- 改进对其他看不见的AE的检测和监测.
- 支持以患者为中心的护理和临床决策.
主要方法:
- 叙述性EHR文本 (进度说明,护理记录,解雇摘要) 使用MedNERN-CR-JA进行处理,这是基于日本BERT的实体识别模型.
- 使用卡普兰-梅尔曲线 (时间到首次发病) 和热图 (症状记录和支持性药物使用) 来可视化不良事件 (AE).
- 分析将接受帕克利塔塞尔 (PTX) 的患者与匹配的对照组进行了比较.
主要成果:
- 帕克利塔塞尔 (PTX) 与肌肉骨症状的显著更高风险相关 (HR,1.77;95% CI:1.57-1.99).
- 卡普兰-梅尔曲线表明PTX接受者早期出现症状.
- 热图显示了复发性症状记录和并发性止痛药的使用,当专注于每三周一次的PTX疗程时,对治疗周期的整合更为明确.
结论:
- 集成的NLP和可视化框架有效地增强了从叙事EHR中主观AE数据的解析.
- 这种方法改善了AE监测,帮助临床决策和以患者为中心的护理.
- 可视化事件和症状模式的时间为药物诱导的不良事件提供了有价值的见解.
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