Session: Machine Learning + Healthcare: Opportunities and challenges
Healthcare has become a hot application topic for machine learning researchers and practitioners in recent years. From breast cancer detection to detecting depression on twitter (public health) to mining for insights in the massive archives of COVID-related literature on the internet, there are a myriad of ways that machine learning has been employed to help aid healthcare workers and scientists. However, because of the complex and critical nature of healthcare, there are many challenges to deploying models in real time, including mitigating risk and providing sufficient explainability. In this talk, I will discuss ways that machine learning researchers have tackled problems in healthcare, as well as the obstacles to successful integration of ML into healthcare.
Bio: Yada Pruksachatkun
Yada Pruksachatkun is a machine learning researcher who specializes in natural language processing and its applications in healthcare. She has led interdisciplinary research efforts with groups of physicians and computer scientists, and worked for Facebook, Microsoft Research, among other companies. Before she delved into the world of machine learning and healthcare, she founded and ran a national volunteer initiative in Thailand that organized extracurricular activity programs for youth in in-patient units in hospitals.
