Accurate Treatment
Accurate treatment research focuses on improving the efficacy and precision of interventions across diverse fields, from medicine and healthcare to language processing and causal inference. Current efforts utilize machine learning models, including deep learning architectures like transformers, recurrent networks, and Gaussian processes, to analyze complex data, predict treatment responses, and optimize treatment strategies. This work is significant for improving patient outcomes in various diseases, enhancing the reliability of causal inference in complex systems, and advancing the development of more effective and personalized therapies.
Papers
Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework
Niloufar Eghbali, Tuka Alhanai, Mohammad M. Ghassemi
RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment
Xuanzhong Chen, Ye Jin, Xiaohao Mao, Lun Wang, Shuyang Zhang, Ting Chen