将自然语言处理应用于患者信息,以识别癌症患者的抑郁问题
Marieke M van Buchem1,2, Anne A H de Hond1,3, Claudio Fanconi1,4
1Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA 94305, United States.
概括
这项研究表明,来自变压器的双向编码器表示 (BERT) 模型可以在癌症患者的信息中识别抑郁症问题. 这些人工智能工具可能有助于早期检测和改善患者护理,但必须解决偏见问题.
科学领域:
- 在瘤学瘤学.
- 数字健康数字健康
- 自然语言处理自然语言处理.
背景情况:
- 抑郁症是癌症患者常见的担忧,影响他们的生活质量和治疗结果.
- 早期发现抑郁症对于及时干预和改善患者护理至关重要.
- 利用来自安全门户网站的患者生成数据为监测心理健康提供了一种新的方法.
研究的目的:
- 开发和评估机器学习模型,用于早期识别癌症患者的抑郁问题.
- 探索自然语言处理的实用性,特别是从变压器 (BERT) 模型的双向编码器表示,使用安全的患者门户消息.
- 评估模型的性能,公平性和可解释性,以确定潜在的抑郁症指标.
主要方法:
- 开发了包括逻辑回归 (LR),支持向量机 (SVM) 和两个BERT模型 (原始和Reddit预训练) 的分类器.
- 模型接受了来自癌症中心的6600条患者信息的训练,这些信息由医疗保健专业人员注释.
- 使用AUROC评分来评估绩效,并检查公平性和可解释性. 还评估了与抑郁症诊断和治疗的相关性.
主要成果:
- 伯特和RedditBERT模型获得了高的AUROC分数 (分别为0.88和0.86),表现优于LR (0.79) 和SVM (0.83).
- 被标记为"关注"的消息的患者显示,抑郁症诊断,抗抑郁药物处方或精神瘤学家转诊的可能性增加.
- 与RedditBERT相比,BERT在人口群体 (性别,种族,种族) 中表现的变化更大,这表明潜在的偏见.
结论:
- 基于BERT的模型显示了癌症患者沟通中早期抑郁问题检测的巨大潜力.
- 人口统计学群体之间的绩效差异需要对潜在的偏见给予仔细关注,并需要进一步研究以缓解.
- 负责任地融入临床环境需要解决偏见,并评估对患者护理和临床负担的实际影响.
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