Automatic identification of clinical trials for which a patient is eligible
is complicated by the fact that trial eligibility is stated in natural
language. A potential solution to this problem is to employ text classification
methods for common types of eligibility criteria. In this study, we focus on
seven common exclusion criteria in cancer trials: prior malignancy, human
immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness,
drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase
III cancer trials with these exclusions annotated at the trial level. We
experiment with common transformer models as well as a new pre-trained clinical
trial BERT model. Our results demonstrate the feasibility of automatically
classifying common exclusion criteria. Additionally, we demonstrate the value
of a pre-trained language model specifically for clinical trials, which yields
the highest average performance across all criteria.

通过使用文本分类方法，我们研究了肿瘤试验中常见的七种排除标准，即先前恶性肿瘤、人免疫缺陷病毒、乙型肝炎、丙型肝炎、精神疾病、药物 / 物质滥用和自身免疫性疾病，结果显示自动分类常见排除标准的可行性，并展示了针对临床试验的预训练语言模型在所有标准上取得的最优性能。

癌症临床试验资格标准的文本分类

Text Classification of Cancer Clinical Trial Eligibility Criteria

Clinical trials are vital in advancing drug development and evidence-based
medicine, but their success is often hindered by challenges in patient
recruitment. In this work, we investigate the potential of large language
models (LLMs) to assist individual patients and referral physicians in
identifying suitable clinical trials from an extensive selection. Specifically,
we introduce TrialGPT, a novel architecture employing LLMs to predict
criterion-level eligibility with detailed explanations, which are then
aggregated for ranking and excluding candidate clinical trials based on
free-text patient notes. We evaluate TrialGPT on three publicly available
cohorts of 184 patients and 18,238 annotated clinical trials. The experimental
results demonstrate several key findings: First, TrialGPT achieves high
criterion-level prediction accuracy with faithful explanations. Second, the
aggregated trial-level TrialGPT scores are highly correlated with expert
eligibility annotations. Third, these scores prove effective in ranking
clinical trials and exclude ineligible candidates. Our error analysis suggests
that current LLMs still make some mistakes due to limited medical knowledge and
domain-specific context understanding. Nonetheless, we believe the explanatory
capabilities of LLMs are highly valuable. Future research is warranted on how
such AI assistants can be integrated into the routine trial matching workflow
in real-world settings to improve its efficiency.

研究使用大型语言模型（LLMs）协助患者和转诊医生从广泛的选择中筛选适合的临床试验，并评估了 TrialGPT 在 184 名患者和 18,238 个注释临床试验的实验结果，表明其高准确性和排名排除不合格候选试验的有效性。