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.

翻译：目的：临床试验对于推进药物干预至关重要，但在选择合格受试者时面临瓶颈。尽管利用电子健康记录进行招募已日益普及，但非结构化医疗文本的复杂性对高效识别受试者提出了挑战。自然语言处理技术作为解决方案应运而生，近期研究聚焦于Transformer模型。本研究旨在评估基于提示的大语言模型在从电子健康记录非结构化医疗笔记中筛选队列任务中的性能。方法：为处理医疗记录，我们选取与试验入选标准最相关的句子，收集与每个入选标准对应的SNOMED CT概念，并基于SNOMED CT本体使用MedCAT对医疗记录进行标注。提取包含与标准相关术语匹配概念的标注句子后，采用基于提示的大语言模型（本研究中使用生成式预训练Transformer）以提取句子作为训练集。为评估其有效性，我们使用2018年n2c2挑战赛数据集测试模型性能，该数据集旨在通过NLP技术基于13项入选标准对311名患者的医疗记录进行分类。结果：本模型总体微观与宏观F值分别为0.9061和0.8060，是该数据集实验中的最高得分之一。结论：本研究基于提示的大语言模型在按入选标准分类患者方面取得了优异评分。此外，我们提出了一种借助SNOMED CT本体的抽取式摘要方法，该方法也可应用于其他医疗文本。