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

Kwon, Bum Chul ; Anand, Vibha ; Severson, Kristen A. ; Ghosh, Soumya ; Sun, Zhaonan ; Frohnert, Brigitte I. ; Lundgren, Markus LU and Ng, Kenney (2021) In IEEE Transactions on Visualization and Computer Graphics 27(9). p.3685-3700
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... (More)

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.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Analytical models, Data models, Data visualization, Diabetes, Task analysis, Disease Progression, Diseases, Hidden Markov Model, Hidden Markov models, Huntingtons, Interpretability, Parkinsons, State Space Model
in
IEEE Transactions on Visualization and Computer Graphics
volume
27
issue
9
pages
16 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • pmid:32275600
  • scopus:85083420813
ISSN
1077-2626
DOI
10.1109/TVCG.2020.2985689
language
English
LU publication?
yes
id
b4778f5a-00e3-49b5-aa81-f54f9e89806c
date added to LUP
2020-05-11 15:30:57
date last changed
2024-04-17 08:23:34
@article{b4778f5a-00e3-49b5-aa81-f54f9e89806c,
  abstract     = {{<p>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.</p>}},
  author       = {{Kwon, Bum Chul and Anand, Vibha and Severson, Kristen A. and Ghosh, Soumya and Sun, Zhaonan and Frohnert, Brigitte I. and Lundgren, Markus and Ng, Kenney}},
  issn         = {{1077-2626}},
  keywords     = {{Analytical models; Data models; Data visualization; Diabetes; Task analysis; Disease Progression; Diseases; Hidden Markov Model; Hidden Markov models; Huntingtons; Interpretability; Parkinsons; State Space Model}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{3685--3700}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Visualization and Computer Graphics}},
  title        = {{DPVis : Visual Analytics with Hidden Markov Models for Disease Progression Pathways}},
  url          = {{http://dx.doi.org/10.1109/TVCG.2020.2985689}},
  doi          = {{10.1109/TVCG.2020.2985689}},
  volume       = {{27}},
  year         = {{2021}},
}