Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient's Point of View
This addresses the challenge of patient recruitment in clinical trials by enabling direct patient-initiated matching, though it is incremental as it adapts existing datasets and methods.
The study tackled the problem of patients self-assessing eligibility for clinical trials using their own language, by creating a new dataset (NLI4PR) and testing open-source LLMs, achieving F1 scores of 56.5 to 71.8 with patient language compared to 64.7 to 73.1 with medical language, showing only a small performance loss.
Recruiting patients to participate in clinical trials can be challenging and time-consuming. Usually, participation in a clinical trial is initiated by a healthcare professional and proposed to the patient. Promoting clinical trials directly to patients via online recruitment might help to reach them more efficiently. In this study, we address the case where a patient is initiating their own recruitment process and wants to determine whether they are eligible for a given clinical trial, using their own language to describe their medical profile. To study whether this creates difficulties in the patient trial matching process, we design a new dataset and task, Natural Language Inference for Patient Recruitment (NLI4PR), in which patient language profiles must be matched to clinical trials. We create it by adapting the TREC 2022 Clinical Trial Track dataset, which provides patients' medical profiles, and rephrasing them manually using patient language. We also use the associated clinical trial reports where the patients are either eligible or excluded. We prompt several open-source Large Language Models on our task and achieve from 56.5 to 71.8 of F1 score using patient language, against 64.7 to 73.1 for the same task using medical language. When using patient language, we observe only a small loss in performance for the best model, suggesting that having the patient as a starting point could be adopted to help recruit patients for clinical trials. The corpus and code bases are all freely available on our Github and HuggingFace repositories.