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Found 4 papers, 0 papers with code
Modeling Disease Progression Trajectories from Longitudinal Observational Data
We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods.
SANVis: Visual Analytics for Understanding Self-Attention Networks
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.
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records.
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical 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.
