# DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

## TL;DR

DPVis is a visual analytics tool that helps clinical researchers interpret and explore disease progression patterns modeled by Hidden Markov Models across various chronic diseases.

## Contribution

The paper introduces DPVis, an interactive visualization system that makes HMM-based disease progression models more interpretable for medical experts.

## Key findings

- Effective in evaluating disease progression models
- Enables visual summarization of disease states
- Supports exploration and comparison of patient subgroups

## Abstract

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.