RAM - EHR : Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024)

Abstract: We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in AUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks.

## introduction electronic health records (ehrs), encompassing detailed information about patients such as symptoms, diagnosis, and medication, are widely used by physicians to deliver patient care. recently, a vast amount of deep learning models have been developed on ehr data @xcite @xcite for various downstream prediction tasks (e.g., disease diagnosis, risk prediction) to facilitate precision healthcare. to further improve the downstream predictive performance, several works attempt to augment the ehr visits with external knowledge. for example, van @xcite and @xcite incorporate additional clinical notes, although these clinical notes can be noisy and contain irrelevant contents for clinical predictions; another solution is to leverage external clinical knowledge graphs (kgs), such as umls @xcite ), * equal contribution. in this work, we propose ram-ehr, a retrievalaugmented framework tailored for clinical predictive tasks on ehrs. instead of leveraging a single knowledge source, ram-ehr collects multiple knowledge sources (e.g., kgs, scientific literature) and converts them to text corpus, which enjoys the merits of a more comprehensive coverage of knowledge in a unified format. then, to obtain unified representations for different knowledge sources, we leverage dense retrieval (dr) @xcite to encode corpus and medical codes as dense vectors, intuitively capturing the semantics of medical codes and addressing the alignment issue between ehr and external knowledge. finally, to reduce irrelevant information, we utilize an llm to summarize the top-retrieved passages into concise and informative knowledge summaries relevant to downstream tasks for each medical code. this process enhances the relevance and utility of the retrieved knowledge for clinical tasks. to leverage external knowledge to assist clinical prediction, we introduce a retrieval-augmented model alongside the local ehr predictive model, which relies solely on patient visit information. the augmented model concatenates summarized passages and medical codes, feeding them into a moderate-size, pre-trained language model. we then co-train the local model and the augmented model with a consistency regularization, which captures the complementary information from patient visits and summarized knowledge and helps the model with better generalization @xcite . we verify the effectiveness of ram-ehr by conducting experiments on two ehr datasets and show that ram-ehr outperforms strong knowledge-enhanced predictive baselines by 3.4% in auroc and 7.2% in aupr on average. our analysis further confirms the advantage of leveraging multi-source external knowledge as well as retrieval augmentation as plugins to assist vanilla ehr predictive models based on visits only. additional studies justify the usefulness of summarized knowledge for assisting clinical prediction tasks. ## conclusion we propose ram-ehr, which uses dense retrieval with multiple knowledge sources and consistency regularization to enhance ehr prediction tasks. experiments on two ehr datasets show the efficacy of ram-ehr over baselines with a gain of aortic valve disorder encompasses congenital, syndromic, and acquired conditions, leading to impaired blood flow and heart functionality. thiazolidinediones are insulin-sensitizing agents used to treat type 2 diabetes, with potential side effects of heart failure.