Enhancing Healthcare Decision-Making with AI: Towards Clinically Applicable ... (Shengpu Tang)
4.7 هزار بار بازدید -
4 ماه پیش
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Title: Enhancing Healthcare Decision-Making with
Title: Enhancing Healthcare Decision-Making with AI: Towards Clinically Applicable Reinforcement Learning
Presenter: Shengpu Tang, PhD candidate in Computer Science and Engineering, University of Michigan
Abstract: Decisions are everywhere in healthcare, ranging from diagnosis to treatments, to care coordination and resource allocation – and these decisions directly impact patient care and outcomes. Reinforcement learning (RL) is a popular AI framework for modeling sequential decision-making and has been successful for various domains such as AlphaGo and chatbots. However, existing RL approaches are often incompatible with healthcare and face unique challenges. In this talk, I will discuss representative works that demonstrate our progress in bringing RL-based treatment recommendations closer to reality, where we address three key challenges: heterogeneous objectives across users, combinatorial action spaces, and safe evaluation before deployment. In addition, I will briefly touch on our ongoing efforts at Michigan Medicine in using AI to reduce hospital-acquired infections. Finally, I will share my vision for the future landscape of human-AI teams in healthcare decision-making.
Bio: Shengpu Tang is a PhD candidate in Computer Science and Engineering at the University of Michigan, advised by Prof. Jenna Wiens. His research focuses on designing and applying artificial intelligence methods to enhance decision making in healthcare, with a particular emphasis on reinforcement learning. His work has been recognized by both top-tier AI conferences (NeurIPS, ICML, including a NeurIPS 2022 “oral”) and leading medical journals (The BMJ, Health Affairs, JAMIA). He served as a main organizer for the ML4H Symposium in 2022 and 2023 and an area chair for CHIL 2024, and he regularly reviews for NeurIPS, ICML, ICLR, AAAI, AISTATS, and KDD. He was a research intern with Microsoft Research in 2022, supervised by Sebastian Kochman and Paul Mineiro. More information is available at: https://shengpu-tang.me
Presenter: Shengpu Tang, PhD candidate in Computer Science and Engineering, University of Michigan
Abstract: Decisions are everywhere in healthcare, ranging from diagnosis to treatments, to care coordination and resource allocation – and these decisions directly impact patient care and outcomes. Reinforcement learning (RL) is a popular AI framework for modeling sequential decision-making and has been successful for various domains such as AlphaGo and chatbots. However, existing RL approaches are often incompatible with healthcare and face unique challenges. In this talk, I will discuss representative works that demonstrate our progress in bringing RL-based treatment recommendations closer to reality, where we address three key challenges: heterogeneous objectives across users, combinatorial action spaces, and safe evaluation before deployment. In addition, I will briefly touch on our ongoing efforts at Michigan Medicine in using AI to reduce hospital-acquired infections. Finally, I will share my vision for the future landscape of human-AI teams in healthcare decision-making.
Bio: Shengpu Tang is a PhD candidate in Computer Science and Engineering at the University of Michigan, advised by Prof. Jenna Wiens. His research focuses on designing and applying artificial intelligence methods to enhance decision making in healthcare, with a particular emphasis on reinforcement learning. His work has been recognized by both top-tier AI conferences (NeurIPS, ICML, including a NeurIPS 2022 “oral”) and leading medical journals (The BMJ, Health Affairs, JAMIA). He served as a main organizer for the ML4H Symposium in 2022 and 2023 and an area chair for CHIL 2024, and he regularly reviews for NeurIPS, ICML, ICLR, AAAI, AISTATS, and KDD. He was a research intern with Microsoft Research in 2022, supervised by Sebastian Kochman and Paul Mineiro. More information is available at: https://shengpu-tang.me
4 ماه پیش
در تاریخ 1402/12/16 منتشر شده
است.
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