  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.
