以自然语言处理为基础的方法,从非结构化的临床记录中检测抗癌剂的常见不良事件:时间对事件分析
Masami Tsuchiya1, Kiminori Shimamoto2, Yoshimasa Kawazoe2
1Keio University Faculty of Pharmacy.
Studies in health technology and informatics
|August 8, 2025
概括
自然语言处理 (NLP) 通过机器学习有效地检测癌症患者的不良事件 (AE). 这种自动化方法显示出在医疗记录中识别抗癌剂的AE的前途.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 副作用是抗癌疗法的常见并发症.
- 准确及时检测AE对于患者安全和治疗管理至关重要.
- 目前在电子健康记录 (EHR) 中检测AE的方法可能是劳动密集型和不完整的.
研究的目的:
- 评估自然语言处理 (NLP) 在识别与抗癌剂相关的副作用方面的有效性.
- 评估专用机器学习模型在大量癌症患者队列中自动检测AE的性能.
主要方法:
- 分析了来自39000多名癌症患者的非识别EHR数据.
- 开发和应用一种使用NLP技术的专门机器学习模型.
- 在接受特定抗癌药物的患者和非使用者之间比较AE发病率.
主要成果:
- 该NLP模型成功地确定了与抗癌药物如capecitabine,oxaliplatin和anthracyclines相关的已知AE.
- 与对照组相比,在接受治疗的患者中观察到明显更高的副作用发生率.
- 该模型在检测有症状的副作用方面表现出很高的有效性,但在罕见疾病和模两可的临床术语方面面临挑战.
结论:
- 在瘤学中,NLP为自动检测不良事件提供了一种有前途的方法.
- 这种自动化方法可以提高从医疗记录中识别AE的能力,特别是对于缺乏实验室或诊断代码的症状.
- 需要进一步完善NLP模型,以解决检测罕见或模两可的记录条件的局限性.
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