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电子医疗记录的自然语言处理识别了心脏保护剂,用于因antracycline引起的心脏毒性
Yoshimasa Kawazoe1, Masami Tsuchiya2, Kiminori Shimamoto3
1Artificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. kawazoe@m.u-tokyo.ac.jp.
Scientific reports
|February 24, 2025
概括
自然语言处理 (NLP) 识别了心脏保护药物,如ARB/ACEI,他类药物和CCB,这些药物可以显著防止由环素诱导的心脏毒性 (AIC). 这证明了NLP.
科学领域:
- 药理学和计算医学 药理学和计算医学
- 药物发现和开发 药物发现和开发
- 临床信息学 临床信息学
背景情况:
- 人环素诱导的心脏毒性 (AIC) 是化疗的严重副作用.
- 药物重新定位为确定现有药物的新治疗用途提供了一个有希望的策略.
- 电子病历 (EMR) 包含有价值的临床信息用于研究.
研究的目的:
- 研究自然语言处理 (NLP) 在药物重新定位方面的潜力.
- 分析心脏保护药物对AIC使用NLP对EMR的预防作用.
- 为了评估特定的药物类别: ангиотензинII受体阻断剂/ ангиотензин转化酶抑制剂 (ARB/ACEI),β-阻断剂 (BBs),他类药物和通道阻断剂 (CCBs).
主要方法:
- 追溯观察性研究设计.
- 利用NLP从EMR中的临床文本中提取心脏毒性信号.
- 倾向性得分匹配用于比较带有和没有特定心脏保护药物的患者组.
- 分析了2935名接受 antracyclines 的患者.
主要成果:
- ARB/ACEI显示危险比率 (HR) 为0.58 (95% CI:0.38-0.88),他类药物HR为0.60 (95% CI:0.38-0.95),CCBsHR为0.63 (95% CI:0.45-0.88),所有这些都表明AIC的显著抑制.
- BBs显示HR为0.71 (95%CI:0.35-1.44),这在统计学上并不显著,可能是由于统计能力有限.
- 通过NLP提取的信号与已知的药理效应相关,验证了该方法.
结论:
- 通过分析EMRs,NLP是一种可行的药物重新定位工具.
- ARB/ACEI,他类药物和CCB显示出针对AIC的显著预防作用.
- 建议进行进一步的多机构研究来证实这些发现.
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