Clinical trials are pivotal in medical research, and NLP can enhance their
success, with application in recruitment. This study aims to evaluate the
generalizability of eligibility classification across a broad spectrum of
clinical trials. Starting with phase 3 cancer trials, annotated with seven
eligibility exclusions, then to determine how well models can generalize to
non-cancer and non-phase 3 trials. To assess this, we have compiled eligibility
criteria data for five types of trials: (1) additional phase 3 cancer trials,
(2) phase 1 and 2 cancer trials, (3) heart disease trials, (4) type 2 diabetes
trials, and (5) observational trials for any disease, comprising 2,490
annotated eligibility criteria across seven exclusion types. Our results show
that models trained on the extensive cancer dataset can effectively handle
criteria commonly found in non-cancer trials, such as autoimmune diseases.
However, they struggle with criteria disproportionately prevalent in cancer
trials, like prior malignancy. We also experiment with few-shot learning,
demonstrating that a limited number of disease-specific examples can partially
overcome this performance gap. We are releasing this new dataset of annotated
eligibility statements to promote the development of cross-disease
generalization in clinical trial classification.

本研究旨在评估自然语言处理对临床试验中资格分类的通用性，结果表明在广泛的临床试验中，模型在处理非癌症试验方面表现出色，但对癌症试验中特定资格的处理相对较难，研究还尝试了少样本学习，证明了少量疾病特异性示例可以部分弥补性能差距，此外，研究提供了一个经过注释的资格陈述数据集，以促进临床试验分类的跨疾病通用性的发展。