Title: 

Integrative Artificial Intelligence for Healthcare

Abstract: 

Developing integrated artificial intelligence frameworks is crucial for hospital operations and medical diagnostics. However, despite the opportunities, there remain challenges on 1) how to effectively learn, share, and combine information from diverse sources into a single setting and 2) how to design models that are practically implementable. In the first part, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that integrate data from multiple modality sources, including tabular, time-series, language, and vision data. Our approach leverages recently developed open-source, pre-trained large models. We show that this framework can consistently and robustly produce models that outperform similar single-modality approaches across various healthcare tasks by 6–33%. In the second part, we introduce Multimodal Multitask Machine Learning for Healthcare (M3H), an explainable framework that consolidates learning for multiple tasks, including supervised binary/multiclass classification, regression, and unsupervised clustering in a single model to better exploit the interactions and dependencies among tasks. It introduces a novel attention mechanism inspired by healthcare considerations that designs a token-based query computation and scaling function that encourages self-exploitation and cross-exploration. The work, in addition, proposes a new explainability metric of the task space to better quantify the dynamics of task learning interdependencies and to automatically detect patterns among task relations. M3H encompasses a wide range of medical tasks and consistently outperforms traditional single-task models by an average of 11.6% across 44 medical tasks. The modular design of both frameworks ensures their generalizability in data processing, task definition, and rapid model prototyping. Combined, HAIM and M3H offer methodological and practical solutions to design integrative artificial intelligence to impact practice. 

Bio:

Yu Ma is a final year PhD student at the MIT Operations Research Center, where she is advised by Prof. Dimitris Bertsimas. Her research focuses on the use of AI methodologies to solve significant problems in healthcare service and policy making. Driven by a commitment to creating real-world impact, she has collaborated with six healthcare institutions and implemented three of her works in practice at Hartford Healthcare, the largest hospital system in Connecticut. In tackling these challenges, her works combine tools from machine learning, optimization, and analytics. She is recognized by the MIT School of Engineering as a Takeda Fellow. In the summer of 2023, she was an applied scientist in eBay’s Recommendation team. Prior to PhD, she obtained a B.A. degree from UC Berkeley in Applied Mathematics.