ArticleJMIR AI2022
Chronic Disease Prediction Using the Common Data Model: Development Study.
Article in JMIR AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Dynamic Individualized Prediction of Binary Clinical Outcomes Using Bayesian Generalized Linear Mixed Models With Application in Scleroderma.Statistics in medicine · 2026Article
- Development of a machine learning model using structured and unstructured features for predicting surgery among patients with carpal tunnel syndrome: development and validation.Scientific reports · 2026Article
- Real-World Safety of Cyproheptadine-Based Appetite Stimulants: An Electronic Health Record-Based Retrospective Cohort Study in Adult Patients.Journal of clinical medicine · 2025Article
- Application of machine learning for early detection of chronic diseases in Africa.Journal of public health research · 2025Article
- Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study.JMIR diabetes · 2025Article
- Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.Scientific reports · 2025Article
- A capsule network-based public health prediction system for chronic diseases: clinical and community implications.Frontiers in public health · 2025Article
- Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data.Frontiers in artificial intelligence · 2025Article
- Behavioral and social predictors of suicidal ideation and attempts among adolescents and young adults.PLOS mental health · 2025Article
- Development and Validation of a Machine Learning-Based Early Warning Model for Lichenoid Vulvar Disease: Prediction Model Development Study.Journal of medical Internet research · 2024Article
- Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.Healthcare (Basel, Switzerland) · 2024Article
- Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS).Journal of medical Internet research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement.
objectiveThis study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models.
methodsIn this study, 4 major chronic diseases-namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease-were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search.
resultsFor the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model's performance, extreme gradient boosting presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards.
conclusionsThis study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data-based CDM and National Health Insurance Corporation examination data that individuals can easily obtain.
Indexed as
Identifiers
What 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.