User-driven Analysis of Longitudinal Health Data with Hidden Markov Models for Clinical Insights

TL;DR

This paper presents DPVis, an interactive visualization system that helps clinical researchers interpret Hidden Markov Model outcomes for understanding disease progression through longitudinal health data.

Contribution

It introduces DPVis, a visual analytics tool enabling clinicians to explore and interpret HMM results in longitudinal health studies, facilitating disease insight extraction.

Findings

Enhanced interpretability of HMM outcomes for clinicians

Improved understanding of disease progression through visualization

Guidelines for clinician-in-the-loop analysis of health data

Abstract

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.