Feb. 12, 2024, 5:42 a.m. | Wenhao Zheng Dongsheng Peng Hongxia Xu Hongtu Zhu Tianfan Fu Huaxiu Yao

cs.LG updates on arXiv.org arxiv.org

The clinical trial is a pivotal and costly process, often spanning multiple years and requiring substantial financial resources. Therefore, the development of clinical trial outcome prediction models aims to exclude drugs likely to fail and holds the potential for significant cost savings. Recent data-driven attempts leverage deep learning methods to integrate multimodal data for predicting clinical trial outcomes. However, these approaches rely on manually designed modal-specific encoders, which limits both the extensibility to adapt new modalities and the ability to …

clinical clinical trial cost cost savings cs.cl cs.lg data data-driven deep learning development drugs financial language language models large language large language models multimodal multiple pivotal prediction prediction models process resources

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Risk Management - Machine Learning and Model Delivery Services, Product Associate - Senior Associate-

@ JPMorgan Chase & Co. | Wilmington, DE, United States

Senior ML Engineer (Speech/ASR)

@ ObserveAI | Bengaluru