arXiv 2402.076452024-02-12cs.CL

使用大型语言模型合成数据检测难治性抑郁症的临床特征

Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models

University of Oxford（牛津大学）；University of Liverpool（利物浦大学）；Mersey Care NHS Foundation Trust（默西护理国民保健信托基金会）；Oxford Health NHS Foundation Trust（牛津健康国民保健信托基金会）；NIHR Oxford Health Biomedical Research Centre（NIHR牛津健康生物医学研究中心）

Isabelle Lorge, Dan W. Joyce, Niall Taylor, Alejo Nevado-Holgado, Andrea Cipriani, Andrey Kormilitzin

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AI总结：

本研究提出基于LLM合成数据和非极大值抑制算法训练BERT跨度提取模型，从电子病历中提取难治性抑郁症的临床预后因素，在真实数据上取得良好性能，为解决医疗数据隐私与专家标注难题提供新途径。

摘要：

难治性抑郁症（DTD）作为一种更广泛、临床视角更全面的抑郁症观点被提出，患者尽管接受治疗，仍持续承受显著负担。我们旨在开发一种基于大型语言模型（LLM）的工具，能够查询常规收集的叙述性（自由文本）电子健康记录（EHR）数据，以定位已发表的、捕捉DTD临床综合征的预后因素。在本研究中，我们使用LLM生成的合成数据（GPT3.5）和非极大值抑制（NMS）算法训练了一个基于BERT的跨度提取模型。由此得到的模型能够在真实临床数据中提取和标记与多种相关阳性和阴性因素相关的文本跨度（即增加或降低患者匹配DTD综合征可能性的文本跨度）。我们展示了，在多达20种不同因素的真实临床数据上可获得良好的的整体性能（极性F1为0.70），并在DTD重要因素子集（如虐待史、情感障碍家族史、疾病严重程度和自杀倾向）上获得高性能（F1为0.85，精确度为0.95），且模型仅在合成数据上训练。我们的结果为未来医疗保健应用展示了希望，特别是在传统上通常需要高度机密的医疗数据和人类专家注释的应用中。

英文摘要：

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden. We sought to develop a Large Language Model (LLM)-based tool capable of interrogating routinely-collected, narrative (free-text) electronic health record (EHR) data to locate published prognostic factors that capture the clinical syndrome of DTD. In this work, we use LLM-generated synthetic data (GPT3.5) and a Non-Maximum Suppression (NMS) algorithm to train a BERT-based span extraction model. The resulting model is then able to extract and label spans related to a variety of relevant positive and negative factors in real clinical data (i.e. spans of text that increase or decrease the likelihood of a patient matching the DTD syndrome). We show it is possible to obtain good overall performance (0.70 F1 across polarity) on real clinical data on a set of as many as 20 different factors, and high performance (0.85 F1 with 0.95 precision) on a subset of important DTD factors such as history of abuse, family history of affective disorder, illness severity and suicidality by training the model exclusively on synthetic data. Our results show promise for future healthcare applications especially in applications where traditionally, highly confidential medical data and human-expert annotation would normally be required.

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