Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Ye, Chengyin; Fu, Tianyun; Hao, Shiying; Zhang, Yan; Wang, Oliver; Jin, Bo; Xia, Minjie; Liu, Modi; Zhou, Xin; Wu, Qian; Yang, Guo; Zhu, Chunqing; Li, Yuming; Culver, Devore S; Alfreds, Shaun T; Stearns, Frank; Sylvester, Karl G.; Widen, Eric; McElhinney, Doff B.; Ling, Xuefeng B.

doi:10.2196/jmir.9268

Cited by 187 publications

(147 citation statements)

References 55 publications

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“…Ye et al used a ML algorithm (XGBoost) to construct and prospectively validated a risk prediction model for future 1‐year risk of incident essential hypertension using electronic health record‐derived data from more than 1.5 million people. The model achieved predictive accuracy of 0.917 and 0.870 in retrospective and prospective (validation) cohorts, respectively.…”

Section: Discussionmentioning

confidence: 99%

Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques

Kanegae

Suzuki

Fukatani

et al. 2019

J of Clinical Hypertension

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It has been estimated that hypertension is the cause of approximately 13% of all deaths worldwide each year. 1 In light of the world's population growth and aging demographic, hypertension is a global burden, along with other cardiovascular and age-related diseases. 2,3 Early intervention with lifestyle modifications and treatment of "prehypertension" may reduce the incidence and long-term consequences of clinical hypertension. [4][5][6][7] Recent guidelines lowered the recommended thresholds for diagnosing hypertension or abnormal "elevated blood pressure (BP)" and the BP goal during antihypertensive therapy. 8-10 Therefore, the ability to predict an individual's risk of developing hypertension would be helpful for clinicians. They could then plan and prescribe personalized lifestyle modifications or make therapeutic decisions designed to prevent or postpone the development of hypertension. There are several models available to predict the risk of new-onset hypertension; these have been developed in Western and Asian countries using traditional statistical methods (eg, Cox regression or logistic regression). 11,12 Arterial stiffness is increasingly being recognized as making an important contribution to increases in systolic BP (SBP) and the development of hypertension in general populations, independent of traditional hypertension risk factors. [13][14][15][16] In addition, arterial AbstractHypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention strategies. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset hypertension. In Japan, Industrial Safety and Health Law requires employers to provide annual health checkups to their employees. We used 2005-2016 health checkup data from 18 258 individuals, at the time of hypertension diagnosis [Year (0)] and in the two previous annual visits [Year (−1) and Year (−2)].Data were entered into models based on machine learning methods (XGBoost and ensemble) or traditional statistical methods (logistic regression). Data were randomly split into a derivation set (75%, n = 13 694) used for model construction and development, and a validation set (25%, n = 4564) used to test performance of the derived models. The best predictor in the XGBoost model was systolic blood pressure during cardio-ankle vascular index measurement at Year (−1). Area under the receiver operator characteristic curve values in the validation cohort were 0.877, 0.881, and 0.859 for the XGBoost, ensemble, and logistic regression models, respectively. We have developed a highly precise prediction model for future hypertension using machine learning methods in a general normotensive population. This could be used to identify at-risk individuals and facilitate earlier non-pharmacological intervention to prevent the future development of hypertension.

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Section: Discussionmentioning

confidence: 99%

Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques

Kanegae

Suzuki

Fukatani

et al. 2019

J of Clinical Hypertension

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“…Univariable analysis was performed on z-score-normalized features, and logistic regression was used to calculate the odds ratios and P values for feature filtering. For multivariate model building, a gradient boosting tree algorithm XGBoost was used for constructing a multivariable prediction model [10][11][12][13][14][15][16] . The baseline learner is the classification and regression tree and the number of trees is selected via cross-validation to avoid over-fitting.…”

Section: Statistical Analysis and Modelling To Predict Recurrence Of mentioning

confidence: 99%

Recurrence of SARS-CoV-2 PCR positivity in COVID-19 patients: a single center experience and potential implications

Huang

Zheng

et al. 2020

Preprint

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IMPORTANCE How to appropriately care for patients who become PCR-negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still not known.Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system if a subset were to be PCR-positive again with reactivated SARS-CoV-2.OBJECTIVE To characterize a single center COVID-19 cohort with and without recurrence of PCR positivity, and develop an algorithm to identify patients at high risk of retest positivity after discharge to inform health care policy and case management decision-making. DESIGN, SETTING, AND PARTICIPANTSA cohort of 414 patients with confirmed SARS-CoV-2 infection, at The Second Affiliated EXPOSURES Polymerase chain reaction (PCR) and IgM-IgG antibody confirmed SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES Univariable and multivariable statistical analysis of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data to develop an algorithm to predict patients at risk of recurrence of PCR positivity.RESULTS 16.7% (95CI: 13.0%-20.3%) patients retest PCR positive 1 to 3 times after discharge, despite being in strict quarantine. The driving factors in the recurrence prediction model included: age, BMI; lowest levels of the blood laboratory tests

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“…Outcome of hypertension is a clinical concept that refers to the death or serious complications (myocardial effective advice. Ye et al used the statewide electronic health record to predict the risk of hypertension within a year using the extreme gradient boosting (XGBoost) method [14]. Park used different machine learning methods to predict high-risk vascular disease in patients with hypertension [15].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

et al. 2019

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The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

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Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Cited by 187 publications

References 55 publications

Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques

Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques

Recurrence of SARS-CoV-2 PCR positivity in COVID-19 patients: a single center experience and potential implications

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

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