User-driven Analysis of Longitudinal Health Data with Hidden Markov Models for Clinical Insights
This work addresses the problem of interpretability in disease progression analysis for clinical researchers, though it is incremental as it builds on existing HMM methods with a visualization tool.
The paper tackled the challenge of interpreting Hidden Markov Model outcomes for disease progression analysis by introducing DPVis, an interactive visualization system that enables clinical researchers to explore HMM results interactively, resulting in a clinician-in-the-loop approach for analyzing longitudinal health data.
A goal of clinical researchers is to understand the progression of a disease through a set of biomarkers. Researchers often conduct observational studies, where they collect numerous samples from selected subjects throughout multiple years. Hidden Markov Models (HMMs) can be applied to discover latent states and their transition probabilities over time. However, it is challenging for clinical researchers to interpret the outcomes and to gain insights about the disease. Thus, this demo introduces an interactive visualization system called DPVis, which was designed to help researchers to interactively explore HMM outcomes. The demo provides guidelines of how to implement the clinician-in-the-loop approach for analyzing longitudinal, observational health data with visual analytics.