Many diagnostic errors occur because clinicians cannot easily access relevant information in patient Electronic Health Records (EHRs). In this work we propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses; our ultimate aim is to increase access to evidence and reduce diagnostic errors. In particular, we propose a Neural Additive Model to make predictions backed by evidence with individualized risk estimates at time-points where clinicians are still uncertain, aiming to specifically mitigate delays in diagnosis and errors stemming from an incomplete differential. To train such a model, it is necessary to infer temporally fine-grained retrospective labels of eventual "true" diagnoses. We do so with LLMs, to ensure that the input text is from before a confident diagnosis can be made. We use an LLM to retrieve an initial pool of evidence, but then refine this set of evidence according to correlations learned by the model. We conduct an in-depth evaluation of the usefulness of our approach by simulating how it might be used by a clinician to decide between a pre-defined list of differential diagnoses.

翻译：许多诊断错误之所以发生，是因为临床医生无法便捷地获取患者电子健康记录（EHRs）中的相关信息。本研究提出一种利用大型语言模型（LLMs）从患者EHR数据中识别特定诊断风险升高或降低的证据片段的方法；我们的最终目标是增加对证据的获取并减少诊断错误。具体而言，我们提出一种神经加性模型（Neural Additive Model），该模型可在临床医生仍不确定的关键时间点，基于证据提供个体化的风险估计，旨在专门缓解因鉴别诊断不完整而导致的诊断延误和错误。为训练此类模型，需推断最终“真实”诊断在时间粒度上的回顾性标签。我们使用LLMs实现这一目标，确保输入文本源自能做出可信诊断之前的时段。我们利用LLM检索初始证据池，随后根据模型学习到的相关性对该证据集进行精炼。我们通过模拟临床医生利用该方法在预设鉴别诊断列表中进行决策的场景，对方法的实用性进行了深入评估。