TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917726526636032 |
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| author | Yue, Ling Xing, Sixue Chen, Jintai Fu, Tianfan |
| author_facet | Yue, Ling Xing, Sixue Chen, Jintai Fu, Tianfan |
| contents | Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the statistical power of the treatment (e.g., a new drug) in curing a certain disease. Clinical trial recruitment has a significant impact on trial success. Forecasting whether the recruitment process would be successful before we run the trial would save many resources and time. This paper develops a novel deep & cross network with large language model (LLM)-augmented text feature that learns semantic information from trial eligibility criteria and predicts enrollment success. The proposed method enables interpretability by understanding which sentence/word in eligibility criteria contributes heavily to prediction. We also demonstrate the empirical superiority of the proposed method (0.7002 PR-AUC) over a bunch of well-established machine learning methods. The code and curated dataset are publicly available at https://anonymous.4open.science/r/TrialEnroll-7E12. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13115 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models Yue, Ling Xing, Sixue Chen, Jintai Fu, Tianfan Machine Learning Computation and Language Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the statistical power of the treatment (e.g., a new drug) in curing a certain disease. Clinical trial recruitment has a significant impact on trial success. Forecasting whether the recruitment process would be successful before we run the trial would save many resources and time. This paper develops a novel deep & cross network with large language model (LLM)-augmented text feature that learns semantic information from trial eligibility criteria and predicts enrollment success. The proposed method enables interpretability by understanding which sentence/word in eligibility criteria contributes heavily to prediction. We also demonstrate the empirical superiority of the proposed method (0.7002 PR-AUC) over a bunch of well-established machine learning methods. The code and curated dataset are publicly available at https://anonymous.4open.science/r/TrialEnroll-7E12. |
| title | TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2407.13115 |