  Objective: Clinical trials are essential for advancing pharmaceutical
interventions, but they face a bottleneck in selecting eligible participants.
Although leveraging electronic health records (EHR) for recruitment has gained
popularity, the complex nature of unstructured medical texts presents
challenges in efficiently identifying participants. Natural Language Processing
(NLP) techniques have emerged as a solution with a recent focus on transformer
models. In this study, we aimed to evaluate the performance of a prompt-based
large language model for the cohort selection task from unstructured medical
notes collected in the EHR. Methods: To process the medical records, we
selected the most related sentences of the records to the eligibility criteria
needed for the trial. The SNOMED CT concepts related to each eligibility
criterion were collected. Medical records were also annotated with MedCAT based
on the SNOMED CT ontology. Annotated sentences including concepts matched with
the criteria-relevant terms were extracted. A prompt-based large language model
(Generative Pre-trained Transformer (GPT) in this study) was then used with the
extracted sentences as the training set. To assess its effectiveness, we
evaluated the model's performance using the dataset from the 2018 n2c2
challenge, which aimed to classify medical records of 311 patients based on 13
eligibility criteria through NLP techniques. Results: Our proposed model showed
the overall micro and macro F measures of 0.9061 and 0.8060 which were among
the highest scores achieved by the experiments performed with this dataset.
Conclusion: The application of a prompt-based large language model in this
study to classify patients based on eligibility criteria received promising
scores. Besides, we proposed a method of extractive summarization with the aid
of SNOMED CT ontology that can be also applied to other medical texts.
