Interpreting Patient Descriptions using Distantly Supervised Similar Case Retrieval

Abstract

Biomedical natural language processing often involves the interpretation of patient descriptions, for instance for diagnosis or for recommending treatments. Current methods, based on biomedical language models, have been found to struggle with such tasks. Moreover, retrieval augmented strategies have only had limited success, as it is rare to find sentences which express the exact type of knowledge that is needed for interpreting a given patient description. For this reason, rather than attempting to retrieve explicit medical knowledge, we instead propose to rely on a nearest neighbour strategy. First, we retrieve text passages that are similar to the given patient description, and are thus likely to describe patients in similar situations, while also mentioning some hypothesis (e.g. a possible diagnosis of the patient). We then judge the likelihood of the hypothesis based on the similarity of the retrieved passages. Identifying similar cases is challenging, however, as descriptions of similar patients may superficially look rather different, among others because they often contain an abundance of irrelevant details. To address this challenge, we propose a strategy that relies on a distantly supervised cross-encoder. Despite its conceptual simplicity, we find this strategy to be effective in practice. CCS CONCEPTS• Applied computing → Life and medical sciences; • Information systems → Information retrieval; • Computing methodologies → Natural language processing.

Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation

“…We also chose 4 specialist LMs: BioBERT [46], Longformer [47], MentalBERT [48], and MentalRoBERTa [48]. BioBERT is suitable for use where biomedical concepts are prevalent, such as electronic medical records [49], patient descriptions [50], and health-related Twitter posts [51]. Longformer is designed for use when text is formed from long documents.…”