Effective Matching of Patients to Clinical Trials using Entity
  Extraction and Neural Re-ranking
Wojciech Kusa Óscar E. Mendoza Petr Knoth Gabriella Pasi Allan Hanbury
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.