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.
Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications.
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records.
Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.