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; Ng, Kenney

doi:10.1109/tvcg.2020.2985689

Cited by 54 publications

(36 citation statements)

References 69 publications

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“…Future application of novel analytical methods (33) such as machine learning and data-driven approaches should increase our understanding of type 1 diabetes pathogenesis and prediction. This may include the application of tools already developed in other settings to visualize data-driven clusters (34) and disease progression models (35,36). These approaches require large and diverse data sets such as those of the T1DI cohort that we hope will pave the way to a more precise approach to prediction and prevention of type 1 diabetes.…”

Section: Discussionmentioning

confidence: 99%

Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S.

Anand

Liu

et al. 2021

Diabetes Care

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To combine prospective cohort studies, by including HLA harmonization, and estimate risk of islet autoimmunity and progression to clinical diabetes. RESEARCH DESIGN AND METHODSFor prospective cohorts in Finland, Germany, Sweden, and the U.S., 24,662 children at increased genetic risk for development of islet autoantibodies and type 1 diabetes have been followed. Following harmonization, the outcomes were analyzed in 16,709 infants-toddlers enrolled by age 2.5 years. RESULTSIn the infant-toddler cohort, 1,413 (8.5%) developed at least one autoantibody confirmed at two or more consecutive visits (seroconversion), 865 (5%) developed multiple autoantibodies, and 655 (4%) progressed to diabetes. The 15-year cumulative incidence of diabetes varied in children with one, two, or three autoantibodies at seroconversion: 45% (95% CI 40-52), 85% (78-90), and 92% (85-97), respectively. Among those with a single autoantibody, status 2 years after seroconversion predicted diabetes risk: 12% (10-25) if reverting to autoantibody negative, 30% (20-40) if retaining a single autoantibody, and 82% (80-95) if developing multiple autoantibodies. HLA-DR-DQ affected the risk of confirmed seroconversion and progression to diabetes in children with stable single-autoantibody status. Their 15-year diabetes incidence for higher-versus lower-risk genotypes was 40% (28-50) vs. 12% . The rate of progression to diabetes was inversely related to age at development of multiple autoantibodies, ranging from 20% per year to 6% per year in children developing multipositivity in #2 years or >7.4 years, respectively. CONCLUSIONSThe number of islet autoantibodies at seroconversion reliably predicts 15-year type 1 diabetes risk. In children retaining a single autoantibody, HLA-DR-DQ genotypes can further refine risk of progression.

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Section: Discussionmentioning

confidence: 99%

Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S.

Anand

Liu

et al. 2021

Diabetes Care

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“…The interpretation of the disease states and their evolution was enabled by an investigation of the model parameters and visualisations. 21…”

Section: Modellingmentioning

confidence: 99%

Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning

Severson

Chahine

Smolensky

et al. 2021

The Lancet Digital Health

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Background Parkinson's disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better patient management and improve clinical trial design. Previous approaches to modelling Parkinson's disease progression assumed static progression trajectories within subgroups and have not adequately accounted for complex medication effects. Our objective was to develop a statistical progression model of Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects. Methods In this longitudinal data study, data were collected for up to 7-years on 423 patients with early Parkinson's disease and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI) longitudinal observational study. A contrastive latent variable model was applied followed by a novel personalised input-output hidden Markov model to define disease states. Clinical significance of the states was assessed using statistical tests on seven key motor or cognitive outcomes (mild cognitive impairment, dementia, dyskinesia, presence of motor fluctuations, functional impairment from motor fluctuations, Hoehn and Yahr score, and death) not used in the learning phase. The results were validated in an independent sample of 610 patients with Parkinson's disease from the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP).

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“…They visualize the number of people infected over time, but detailed views about individual patient contacts are not the focus. Machine learning methods working with visual analytics have been proposed [16,34], and more specifically for disease progression pathways [33]. They have been applied to the problem of infection control [42], but in this closely related work the transmission pathway was not reconstructed.…”

Section: Related Workmentioning

confidence: 99%

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Baumgartl

Petzold

Wunderlich

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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

Cited by 54 publications

References 69 publications

Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S.

Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S.

Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

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