Language Interaction Network for Clinical Trial Approval Estimation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913346416017408 |
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| author | Gao, Chufan Fu, Tianfan Sun, Jimeng |
| author_facet | Gao, Chufan Fu, Tianfan Sun, Jimeng |
| contents | Clinical trial outcome prediction seeks to estimate the likelihood that a clinical trial will successfully reach its intended endpoint. This process predominantly involves the development of machine learning models that utilize a variety of data sources such as descriptions of the clinical trials, characteristics of the drug molecules, and specific disease conditions being targeted. Accurate predictions of trial outcomes are crucial for optimizing trial planning and prioritizing investments in a drug portfolio. While previous research has largely concentrated on small-molecule drugs, there is a growing need to focus on biologics-a rapidly expanding category of therapeutic agents that often lack the well-defined molecular properties associated with traditional drugs. Additionally, applying conventional methods like graph neural networks to biologics data proves challenging due to their complex nature. To address these challenges, we introduce the Language Interaction Network (LINT), a novel approach that predicts trial outcomes using only the free-text descriptions of the trials. We have rigorously tested the effectiveness of LINT across three phases of clinical trials, where it achieved ROC-AUC scores of 0.770, 0.740, and 0.748 for phases I, II, and III, respectively, specifically concerning trials involving biologic interventions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06662 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Language Interaction Network for Clinical Trial Approval Estimation Gao, Chufan Fu, Tianfan Sun, Jimeng Biomolecules Computation and Language Machine Learning Clinical trial outcome prediction seeks to estimate the likelihood that a clinical trial will successfully reach its intended endpoint. This process predominantly involves the development of machine learning models that utilize a variety of data sources such as descriptions of the clinical trials, characteristics of the drug molecules, and specific disease conditions being targeted. Accurate predictions of trial outcomes are crucial for optimizing trial planning and prioritizing investments in a drug portfolio. While previous research has largely concentrated on small-molecule drugs, there is a growing need to focus on biologics-a rapidly expanding category of therapeutic agents that often lack the well-defined molecular properties associated with traditional drugs. Additionally, applying conventional methods like graph neural networks to biologics data proves challenging due to their complex nature. To address these challenges, we introduce the Language Interaction Network (LINT), a novel approach that predicts trial outcomes using only the free-text descriptions of the trials. We have rigorously tested the effectiveness of LINT across three phases of clinical trials, where it achieved ROC-AUC scores of 0.770, 0.740, and 0.748 for phases I, II, and III, respectively, specifically concerning trials involving biologic interventions. |
| title | Language Interaction Network for Clinical Trial Approval Estimation |
| topic | Biomolecules Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2405.06662 |