Interpreting patient descriptions using distantly supervised similar case retrieval

Alghanmi, Israa, Espinosa-Anke, Luis ORCID: https://orcid.org/0000-0001-6830-9176 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2022. Interpreting patient descriptions using distantly supervised similar case retrieval. Presented at: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 11-15 July 2022. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, pp. 460-470. 10.1145/3477495.3532003

Preview

PDF - Accepted Post-Print Version Download (619kB) | Preview

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Computer Science & Informatics
Publisher:	ACM
ISBN:	978-1-4503-8732-3/22/0
Date of First Compliant Deposit:	25 May 2022
Date of Acceptance:	31 March 2022
Last Modified:	14 Jun 2024 15:19
URI:	https://orca.cardiff.ac.uk/id/eprint/150044

Actions (repository staff only)

Edit Item