Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development
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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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| _version_ | 1866915183509635072 |
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| author | Gao, Chufan Pradeepkumar, Jathurshan Das, Trisha Thati, Shivashankar Sun, Jimeng |
| author_facet | Gao, Chufan Pradeepkumar, Jathurshan Das, Trisha Thati, Shivashankar Sun, Jimeng |
| contents | Background The cost of drug discovery and development is substantial, with clinical trial outcomes playing a critical role in regulatory approval and patient care. However, access to large-scale, high-quality clinical trial outcome data remains limited, hindering advancements in predictive modeling and evidence-based decision-making.
Methods We present the Clinical Trial Outcome (CTO) benchmark, a fully reproducible, large-scale repository encompassing approximately 125,000 drug and biologics trials. CTO integrates large language model (LLM) interpretations of publications, trial phase progression tracking, sentiment analysis from news sources, stock price movements of trial sponsors, and additional trial-related metrics. Furthermore, we manually annotated a dataset of clinical trials conducted between 2020 and 2024 to enhance the quality and reliability of outcome labels.
Results The trial outcome labels in the CTO benchmark agree strongly with expert annotations, achieving an F1 score of 94 for Phase 3 trials and 91 across all phases. Additionally, benchmarking standard machine learning models on our manually annotated dataset revealed distribution shifts in recent trials, underscoring the necessity of continuously updated labeling approaches.
Conclusions By analyzing CTO's performance on recent clinical trials, we demonstrate the ongoing need for high-quality, up-to-date trial outcome labels. We publicly release the CTO knowledge base and annotated labels at https://chufangao.github.io/CTOD, with regular updates to support research on clinical trial outcomes and inform data-driven improvements in drug development. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_10292 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development Gao, Chufan Pradeepkumar, Jathurshan Das, Trisha Thati, Shivashankar Sun, Jimeng Artificial Intelligence Computation and Language Machine Learning Background The cost of drug discovery and development is substantial, with clinical trial outcomes playing a critical role in regulatory approval and patient care. However, access to large-scale, high-quality clinical trial outcome data remains limited, hindering advancements in predictive modeling and evidence-based decision-making. Methods We present the Clinical Trial Outcome (CTO) benchmark, a fully reproducible, large-scale repository encompassing approximately 125,000 drug and biologics trials. CTO integrates large language model (LLM) interpretations of publications, trial phase progression tracking, sentiment analysis from news sources, stock price movements of trial sponsors, and additional trial-related metrics. Furthermore, we manually annotated a dataset of clinical trials conducted between 2020 and 2024 to enhance the quality and reliability of outcome labels. Results The trial outcome labels in the CTO benchmark agree strongly with expert annotations, achieving an F1 score of 94 for Phase 3 trials and 91 across all phases. Additionally, benchmarking standard machine learning models on our manually annotated dataset revealed distribution shifts in recent trials, underscoring the necessity of continuously updated labeling approaches. Conclusions By analyzing CTO's performance on recent clinical trials, we demonstrate the ongoing need for high-quality, up-to-date trial outcome labels. We publicly release the CTO knowledge base and annotated labels at https://chufangao.github.io/CTOD, with regular updates to support research on clinical trial outcomes and inform data-driven improvements in drug development. |
| title | Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2406.10292 |