ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Modeling Disease Progression Trajectories from Longitudinal Observational Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 7 citations in OpenAlex.
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- Generative AI-augmented transcriptomic and microbiome analysis across inflammatory and fibrotic disease states in Crohn's disease.Frontiers in artificial intelligence · 2026Article
- Investigating the impact of steroid dependence on gastrointestinal surgical outcomes from UK Biobank.Scientific reports · 2024Article
- Population longitudinal analysis of Gait Profile Score and North Star Ambulatory Assessment in children with Duchenne muscular dystrophy.CPT: pharmacometrics & systems pharmacology · 2024Article
- Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children.Diabetologia · 2023Article
- Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes.Diabetes · 2022Article
- Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories.Nature communications · 2022Article
- Islet Autoantibody Type-Specific Titer Thresholds Improve Stratification of Risk of Progression to Type 1 Diabetes in Children.Diabetes care · 2022Article
- 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.Diabetes care · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
Abstract
Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.
Indexed as
Identifiers
33936441PMC8075441W3111880210What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.