In this study, we introduce ExBEHRT, an extended version of BEHRT (BERT applied to electronic health records), and apply different algorithms to interpret its results. While BEHRT considers only diagnoses and patient age, we extend the feature space to several multimodal records, namely demographics, clinical characteristics, vital signs, smoking status, diagnoses, procedures, medications, and laboratory tests, by applying a novel method to unify the frequencies and temporal dimensions of the different features. We show that additional features significantly improve model performance for various downstream tasks in different diseases. To ensure robustness, we interpret model predictions using an adaptation of expected gradients, which has not been previously applied to transformers with EHR data and provides more granular interpretations than previous approaches such as feature and token importances. Furthermore, by clustering the model representations of oncology patients, we show that the model has an implicit understanding of the disease and is able to classify patients with the same cancer type into different risk groups. Given the additional features and interpretability, ExBEHRT can help make informed decisions about disease trajectories, diagnoses, and risk factors of various diseases.

翻译：本研究提出ExBEHRT——一种BEHRT（将BERT应用于电子健康记录）的扩展版本，并应用多种算法对其结果进行解释。鉴于BEHRT仅纳入诊断和患者年龄，我们通过提出一种新颖方法统一不同特征的频率与时间维度，将特征空间扩展至多种模态数据，包括人口统计学信息、临床特征、生命体征、吸烟状态、诊断、手术操作、用药及实验室检查。研究表明，额外特征显著提升了模型在不同疾病下游任务中的性能。为确保鲁棒性，我们采用预期梯度法的改进版本解释模型预测——该方法此前尚未应用于基于电子健康记录数据的Transformer模型，且比特征重要性或令牌重要性等现有方法提供了更细粒度的解释。此外，通过对肿瘤患者模型表征进行聚类分析，我们发现模型具备对疾病的隐含理解能力，可将同种癌症患者划分为不同风险组。凭借增强的特征空间与可解释性，ExBEHRT能够辅助针对多种疾病的病程轨迹、诊断及风险因素做出明智决策。