Improving Neural Models for Radiology Report Retrieval with Lexicon-based Automated Annotation

Anonymous

Paper Link: https://openreview.net/forum?id=EjZROozGRQK

Paper Type: Short paper (up to four pages of content + unlimited references and appendices)

Abstract: Many clinical informatics tasks that are based on electronic health records (EHR) need relevant patient cohorts to be selected based on findings, symptoms and diseases. Frequently, these conditions are described in radiology reports which can be retrieved using information retrieval (IR) methods. The latest of these techniques utilize neural IR models such as BERT trained on clinical text. However, these methods still lack semantic understanding of the underlying clinical conditions as well as ruled out findings, resulting in poor precision during retrieval. In this paper we combine clinical finding detection with supervised query match learning. Specifically, we use lexicon-driven concept detection to detect relevant findings in sentences. These findings are used as queries to train a Sentence-BERT (SBERT) model using triplet loss on matched and unmatched query-sentence pairs. We show that the proposed supervised training task remarkably improves the retrieval performance of SBERT. The trained model generalizes well to unseen queries and reports from different collections.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Luyao Shi

Copyright Consent Name And Address: IBM Research, Almaden Research Center, 650 Harry Rd, San Jose, CA 95120