ArticleJournal of the American Medical Informatics Association : JAMIA
Predicting changes in hypertension control using electronic health records from a chronic disease management program.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Toward an Interdisciplinary Approach to Constructing Care Delivery Pathways From Electronic Health Care Databases to Support Integrated Care in Chronic Conditions: Systematic Review of Quantification and Visualization Methods.Journal of medical Internet research · 2023Pooled it
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- Trends and Gaps in Digital Precision Hypertension Management: Scoping Review.Journal of medical Internet research · 2025Article
- Leveraging Electronic Health Records to Predict the Risk of Acute Kidney Injury after Allogeneic Hematopoietic Cell Transplantation.Life (Basel, Switzerland) · 2024Article
- DeepMPM: a mortality risk prediction model using longitudinal EHR data.BMC bioinformatics · 2022Article
- A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk.Scientific reports · 2022Article
- Review
- Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble.Frontiers in cardiovascular medicine · 2021Review
- Artificial Intelligence and Hypertension: Recent Advances and Future Outlook.American journal of hypertension · 2020Review
- Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational intelligence.Clinical and experimental nephrology · 2020Article
- CaliForest: Calibrated Random Forest for Health Data.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- Article
- Synthesizing electronic health records using improved generative adversarial networks.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension.Current hypertension reports · 2018Review
- Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.Artificial intelligence in medicine · 2017Article
- Accounting for misclassification in electronic health records-derived exposures using generalized linear finite mixture models.Health services & outcomes research methodology · 2017Article
- Combining Contrast Mining with Logistic Regression To Predict Healthcare Utilization in a Managed Care Population.Applied clinical informatics · 2017Article
- Improving condition severity classification with an efficient active learning based framework.Journal of biomedical informatics · 2016Article
- Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program.IEEE journal of biomedical and health informatics · 2016Article
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.
Funding
Abstract
objectiveCommon chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control.
methodIn a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier.
resultsThe most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780).
conclusionsThis study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans.
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.