Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking

TL;DR

This paper presents a novel pipeline combining data enrichment and a Transformer-based re-ranking schema to improve the retrieval of relevant clinical trials by effectively matching patient descriptions with trial eligibility criteria.

Contribution

It introduces a new approach that leverages entity extraction, negation detection, and a two-step Transformer training schema for enhanced clinical trial retrieval accuracy.

Findings

Relevance scores are significantly improved by focusing on the eligibility criteria section.

Enrichment techniques boost the retrieval of relevant trials.

The re-ranking schema improves precision by 15% over baseline models.

Abstract

Clinical trials (CTs) often fail due to inadequate patient recruitment. This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both queries and documents during the first retrieval stage, and (ii) a novel re-ranking schema that uses a Transformer network in a setup adapted to this task by leveraging the structure of the CT documents. We use named entity recognition and negation detection in both patient description and the eligibility section of CTs. We further classify patient descriptions and CT eligibility criteria into current, past, and family medical conditions. This extracted information is used to boost the importance of disease and drug mentions in both query and index for lexical retrieval. Furthermore, we propose a two-step training schema for the Transformer network used to re-rank the results from the lexical retrieval. The first step focuses on matching patient information with the descriptive sections of trials, while the second step aims to determine eligibility by matching patient information with the criteria section. Our findings indicate that the inclusion criteria section of the CT has a great influence on the relevance score in lexical models, and that the enrichment techniques for queries and documents improve the retrieval of relevant trials. The re-ranking strategy, based on our training schema, consistently enhances CT retrieval and shows improved performance by 15\% in terms of precision at retrieving eligible trials. The results of our experiments suggest the benefit of making use of extracted entities. Moreover, our proposed re-ranking schema shows promising effectiveness compared to larger neural models, even with limited training data.