Probabilistic Machine Learning for Healthcare
It provides a comprehensive overview for researchers and practitioners in healthcare AI, but is incremental as it reviews existing methods without introducing new techniques.
This review examines how probabilistic machine learning can advance healthcare by addressing challenges like calibration and missing data in predictive models, and explores its utility in phenotyping, generative models, and reinforcement learning for clinical use cases.
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.