Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| author | Yang, Yumeng Gilliam, Ashley Ludmir, Ethan B Roberts, Kirk |
| author_facet | Yang, Yumeng Gilliam, Ashley Ludmir, Ethan B Roberts, Kirk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17135 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases Yang, Yumeng Gilliam, Ashley Ludmir, Ethan B Roberts, Kirk Computation and Language Machine Learning Quantitative Methods 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. |
| title | Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases |
| topic | Computation and Language Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2403.17135 |