ArticleJAMIA open2019
A probabilistic disease progression modeling approach and its application to integrated Huntington's disease observational data.
Article in JAMIA open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Health state utility estimates for value assessments of novel treatments in Huntington's disease: a systematic literature review.Health and quality of life outcomes · 2024Pooled it
- Modeling Disease Progression and Placebo Response in Huntington Disease: Insights From Enroll-HD and GENERATION HD1 Cohorts.Neurology · 2025Trial
- Decomposing heterogeneity in disease progression speeds and pathways.NPJ digital medicine · 2026Article
- From genes to trajectories: mapping genetic influences on Huntington's disease progression.Bioinformatics (Oxford, England) · 2026Article
- Investigating the impact of steroid dependence on gastrointestinal surgical outcomes from UK Biobank.Scientific reports · 2024Article
- Data-driven Huntington's disease progression modelling and estimation of societal cost in the UK.Royal Society open science · 2024Article
- Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data.Translational lung cancer research · 2024Article
- New clinical trial design in precision medicine: discovery, development and direction.Signal transduction and targeted therapy · 2024Review
- Large-scale screening of clinical assessments to distinguish between states in the Integrated HD Progression Model (IHDPM).Frontiers in aging neuroscience · 2024Article
- Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- The temporal event-based model: Learning event timelines in progressive diseases.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.Scientific reports · 2022Article
- Dynamic modeling of hospitalized COVID-19 patients reveals disease state-dependent risk factors.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories.Nature communications · 2022Article
- Predictors of functional outcomes in patients with facioscapulohumeral muscular dystrophy.Brain : a journal of neurology · 2021Article
- Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease.Neurology. Genetics · 2021Article
- What is the Pathogenic CAG Expansion Length in Huntington's Disease?Journal of Huntington's disease · 2021Review
- Enroll-HD: An Integrated Clinical Research Platform and Worldwide Observational Study for Huntington's Disease.Frontiers in neurology · 2021Review
- Modeling Disease Progression Trajectories from Longitudinal Observational Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveChronic diseases often have long durations with slow, nonlinear progression and complex, and multifaceted manifestation. Modeling the progression of chronic diseases based on observational studies is challenging. We developed a framework to address these challenges by building probabilistic disease progression models to enable better understanding of chronic diseases and provide insights that could lead to better disease management. MATERIALS AND
methodsWe developed a framework to build probabilistic disease progression models using observational medical data. The framework consists of two steps. The first step determines the number of disease states. The second step builds a probabilistic disease progression model with the determined number of states. The model discovers typical states along the trajectory of the target disease, learns the characteristics of these states, and transition probabilities between the states. We applied the framework to an integrated observational HD dataset curated from four recent observational HD studies.
resultsThe resulting HD progression model identified nine disease states. Compared to state-of-art HD staging system, the model 1) covers wider range of HD progression; 2) is able to quantitatively describe complex changes around the time of clinical diagnosis; 3) discovers multiple potential HD progression pathways; and 4) reveals expected time durations of the identified states. DISCUSSION AND
conclusionThe proposed framework addresses practical challenges in observational data and can help enhance the understanding of progression of chronic diseases. The framework could be applied to other chronic diseases with the help of clinical knowledge.
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