期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 27, 期 9, 页码 3685-3700出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2020.2985689
关键词
Hidden Markov models; Diseases; Analytical models; Diabetes; Data visualization; Task analysis; Data models; Disease progression; hidden markov model; state space model; diabetes; huntington's; parkinson's; interpretability
资金
- JDRF [1-IND-2019-717-I-X, 1-SRA-2019-722-I-X, 1-SRA-2019723-I-X, 1-SRA-2019-719-I-X, 1-SRA-2019-721-I-X, 1-SRA-2019-720-I-X]
Clinical researchers use disease progression models to understand patient status and use a method that utilizes a small number of states to describe disease progression. Hidden Markov models and its variants can discover these states and make inferences of a patient's health states. Through a design study, DPVis successfully integrates model parameters and outcomes into interpretable and interactive visualizations.
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 article, 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.
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