[Thesis defense] Reliable and trustworthy AI for evidence-based clinical decision support in cancer care.
Speaker
Intae Moon
CSAIL
Date: 10:00 – 11:00 am, May 2nd (Thursday)
Location: 32-G882 (Hewlett)
Zoom link: https://mit.zoom.us/j/97554081636
Thesis committee: Alexander Gusev (advisor, Dana-Farber Cancer Institute/Harvard Medical School), Marzyeh Ghassemi (advisor, MIT), and Peter Szolovits (MIT)
Abstract:
The integration of cutting-edge AI methods with real-world clinical data has moved from being a novelty to a necessity in oncology. However, the deployment of AI faces challenges, including the complexity of reliably modeling longitudinal Electronic Health Records (EHR) characterized by missing data and frequent patient drop-outs, patient heterogeneity which leads to disparities in AI performance, and the need for validating AI models' clinical benefits, especially in managing challenging cancer cases. In this thesis defense talk, I will present my research focused on addressing these challenges: developing a continuous time model-based time-to-event regression framework to improve the prediction of clinically meaningful patient outcomes from irregularly sampled EHR data; utilizing data and algorithm-driven approaches to mitigate AI performance disparity for predicting cancer-associated adverse events across diverse patient demographics; and developing an AI-based decision support tool that integrates genomics and clinical data for evidence-based cancer care, with a focus on improving management of difficult-to-treat cancer cases. Our work contributes towards transforming cancer care through reliable and trustworthy AI-driven clinical decision support.
Brief Bio:
Intae Moon is a final year PhD student in Electrical Engineering and Computer Science (EECS) at MIT, advised by Professor Marzyeh Ghassemi (MIT CSAIL) and Professor Alexander Gusev (HMS/DFCI). At the intersection of medicine and AI, his research is focused on leveraging clinical data to develop evidence-based AI decision support tools, aimed at improving cancer patient care while addressing the risks of AI bias. His research work has been featured in MIT News, DFCI News, and Nature Portfolio. His commitment to research and teaching has been acknowledged with the 2022 Charles J. Epstein Trainee Awards for Excellence in Human Genetics Research and the 2022 Carlton E. Tucker Award for teaching excellence.
Location: 32-G882 (Hewlett)
Zoom link: https://mit.zoom.us/j/97554081636
Thesis committee: Alexander Gusev (advisor, Dana-Farber Cancer Institute/Harvard Medical School), Marzyeh Ghassemi (advisor, MIT), and Peter Szolovits (MIT)
Abstract:
The integration of cutting-edge AI methods with real-world clinical data has moved from being a novelty to a necessity in oncology. However, the deployment of AI faces challenges, including the complexity of reliably modeling longitudinal Electronic Health Records (EHR) characterized by missing data and frequent patient drop-outs, patient heterogeneity which leads to disparities in AI performance, and the need for validating AI models' clinical benefits, especially in managing challenging cancer cases. In this thesis defense talk, I will present my research focused on addressing these challenges: developing a continuous time model-based time-to-event regression framework to improve the prediction of clinically meaningful patient outcomes from irregularly sampled EHR data; utilizing data and algorithm-driven approaches to mitigate AI performance disparity for predicting cancer-associated adverse events across diverse patient demographics; and developing an AI-based decision support tool that integrates genomics and clinical data for evidence-based cancer care, with a focus on improving management of difficult-to-treat cancer cases. Our work contributes towards transforming cancer care through reliable and trustworthy AI-driven clinical decision support.
Brief Bio:
Intae Moon is a final year PhD student in Electrical Engineering and Computer Science (EECS) at MIT, advised by Professor Marzyeh Ghassemi (MIT CSAIL) and Professor Alexander Gusev (HMS/DFCI). At the intersection of medicine and AI, his research is focused on leveraging clinical data to develop evidence-based AI decision support tools, aimed at improving cancer patient care while addressing the risks of AI bias. His research work has been featured in MIT News, DFCI News, and Nature Portfolio. His commitment to research and teaching has been acknowledged with the 2022 Charles J. Epstein Trainee Awards for Excellence in Human Genetics Research and the 2022 Carlton E. Tucker Award for teaching excellence.