Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning
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Abstract
BACKGROUND: As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. OBJECTIVE: The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. METHODS: Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual. RESULTS: The 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension. CONCLUSIONS: With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.
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The Ye et al. model has been deployed in Maine, United States, to assist healthcare providers in identifying high-risk populations and promoting individualized treatment.
“Demonstrating excellent performance, this model has been deployed in Maine, the United States, to assist healthcare providers in identifying high-risk populations and promoting individualized treatment [27].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [27]
Ye et al. (2018) developed and validated a ML model on EHRs of more than 1.5 million individuals to predict the risk of incident essential hypertension within the next year.
“To inform resource allocation and enhance precision medicine in cardiovascular diseases, Ye et al. (2018) developed and validated a ML model on EHRs of more than 1.5 million individuals to predict the risk of incidence essential hypertension within the next year [27].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [27]
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- Authors
- Ye, C., Fu, T., Hao, S., Zhang, Y., Wang, O., Jin, B., Xia, M., Liu, M., Zhou, X., Wu, Q., Guo, Y., Zhu, C., Li, Y.-M., Culver, D. S., Alfreds, S. T., Stearns, F., Sylvester, K. G., Widen, E., McElhinney, D., & Ling, X.
- Affiliations
- Hangzhou Normal University (CN)
- Lucile Packard Children's Hospital (US)
- First Hospital of Shijiazhuang (CN)
- Pingjin Hospital (CN)
- China Electric Power Research Institute (CN)
- Zhejiang University (CN)
- Stanford University (US)
- Corresponding author
- Ling, Xuefeng
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-01-30
- Volume
- 20
- Issue
- 1
- Pages
- e22
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5811646
- OpenAlex
- W2786814109
- Cited by
- 250
- References
- 61
- Citations by year
- 2026: 18 · 2025: 33 · 2024: 46 · 2023: 37 · 2022: 42 · 2021: 33 · 2020: 21 · 2019: 14 · 2018: 5
- FWCI
- 10.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81402762)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- hypertension incidence, essential hypertension, risk prediction model, XGBoost, electronic health records, social determinants of health, type 2 diabetes, cardiovascular disease, mental illness, socioeconomic disparities, community factors, prospective validation
- MeSH terms
- Machine Learning, Aged, Female, Humans, Hypertension, Male, Middle Aged, Prospective Studies, Retrospective Studies, Risk Factors, Cohort Studies, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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