在甲状腺癌中使用自然语言处理预测与健康相关的生活质量变化
Ruixue Lian1,2, Vivian Hsiao1,3, Juwon Hwang4
1University of Wisconsin, Madison, USA.
Intelligence-based medicine
|September 4, 2023
概括
这项研究开发了一种使用深度学习的自然语言处理 (NLP) 管道,从采访中预测患者健康相关生活质量 (HRQOL) 轨迹. 带有数据增强的BERT模型实现了76.3%的AUC,表现优于基线方法.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 患者报告结果 (PRO) 对于测量与健康相关的生活质量 (HRQOL) 和治疗优先级至关重要.
- 目前的PRO采集方法,如调查,并未纳入常规临床护理.
- 需要开发自动化方法来从患者语言中提取PRO.
研究的目的:
- 为提取HRQOL轨迹创建一个初步的自然语言处理 (NLP) 管道.
- 利用深度学习模型来分析患者语言以获得HRQOL洞察力.
- 评估NLP模型在预测HRQOL变化的表现.
主要方法:
- 基于BERT的NLP管道是使用转录的患者访谈和HRQOL评估来开发的.
- 该管道预测了HRQOL轨迹,使用SF-12物理和心理组件分数 (PCS和MCS) 和THYCA-QoL分数.
- 实施了数据增强技术,包括GPT-2合成样本和序列对配,以提高模型性能.
主要成果:
- 带有GPT-2增强的BERT模型在分类HRQOL准确性 (PCS) 中获得了76.3%的AUC.
- 这显著超过了基线模型 (逻辑回归,用袋子词的SVM),该模型的AUC为59.9%.
- 序列对序列配对增强与BERT模型产生了AUC的71.2%.
结论:
- NLP显示了从非结构化的患者叙述中提取PRO的潜力.
- 未来的应用可能包括评估和预测患者HRQOL对治疗的反应.
- 通过更大的数据集和新数据,可以实现对分类模型性能的进一步改进.
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