Bayesian decision theory provides an elegant framework for acting optimally under uncertainty when tractable posterior distributions are available.
no code implementations • 9 Apr 2021 • Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoonyoung Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Xu Min, Sanjib Basak, Mohamed Ghalwash, Zach Shahn, Parthasararathy Suryanarayanan, Italo Buleje, Shannon Harrer, Sarah Miller, Amol Rajmane, Colin Walsh, Jonathan Wanderer, Gigi Yuen Reed, Kenney Ng, Daby Sow, Bradley A. Malin
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
no code implementations • 9 Dec 2020 • Bum Chul Kwon, Peter Achenbach, Jessica L. Dunne, William Hagopian, Markus Lundgren, Kenney Ng, Riitta Veijola, Brigitte I. Frohnert, Vibha Anand, the T1DI Study Group
We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods.
In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions.
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records.
Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset.
The proposed method is favorable for healthcare applications because in additional to improved prediction performance, relationships among the different risks and risk factors are also identified.