Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension

Krittanawong, Chayakrit; Bomback, Andrew S.; Baber, Usman; Bangalore, Sripal; Messerli, Franz H.; Tang, W.H. Wilson

doi:10.1007/s11906-018-0875-x

Cited by 70 publications

(49 citation statements)

References 132 publications

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“…While convolutional neural network (CNN) processing, combines several layers and apples to image classification and segmentation 3 – 5 . We have previously described technical details of each of these algorithms 6 – 8 , but no consensus has emerged to guide the selection of specific algorithms for clinical application within the field of cardiovascular medicine. Although selecting optimal algorithms for research questions and reproducing algorithms in different clinical datasets is feasible, the clinical interpretation and judgement for implementing algorithms are very challenging.…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction in cardiovascular diseases: a meta-analysis

Krittanawong

Virk

Bangalore

et al. 2020

Sci Rep

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262

114

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Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84–0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85–0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81–0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81–0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83–0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.

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Section: Introductionmentioning

confidence: 99%

Machine learning prediction in cardiovascular diseases: a meta-analysis

Krittanawong

Virk

Bangalore

et al. 2020

Sci Rep

Self Cite

262

114

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“…It is one of the main scores used to indicate hypertension. Many methods utilizing machine learning (ML) techniques are used in risk models of hypertension, e.g., artificial neural network, support vector machine, random forest, naive bayes classifier, gradient boosting machines, decision tree, and logistic regression [18][19][20]. Echouffo-Tcheugui et al systematical reviewed the performance of such algorithms [20], and Krittanawong et al gave a comprehensive review on the prediction of hypertension using artificial intelligence [19].…”

Section: Introductionmentioning

confidence: 99%

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

et al. 2020

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Background and objective Hypertension, a global burden, is associated with several risk factors and can be treated by lifestyle modifications and medications. Prediction and early diagnosis is important to prevent related health complications. The objective is to construct and compare predictive models to identify individuals at high risk of developing hypertension without the need of invasive clinical procedures. Methods This is a cross-sectional study using 987 records of Qataris and long-term residents aged 18 + years from Qatar Biobank. Percentages were used to summarize data and chi-square tests to assess associations. Predictive models of hypertension were constructed and compared using three supervised machine learning algorithms: decision tree, random forest, and logistics regression using 5-fold cross-validation. The performance of algorithms was assessed using accuracy, positive predictive value (PPV), sensitivity, F-measure, and area under the receiver operating characteristic curve (AUC). Stata and Weka were used for analysis. Results Age, gender, education level, employment, tobacco use, physical activity, adequate consumption of fruits and vegetables, abdominal obesity, history of diabetes, history of high cholesterol, and mother's history high blood pressure were important predictors of hypertension. All algorithms showed more or less similar performances:

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“…However, readers must know their application and limitations. While conventional statistics tend to emphasize inference, machine learning emphasizes prediction [46]. There may be a lack of well-understood relationships between independent and dependent variables.…”

Section: Discussionmentioning

confidence: 99%

Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals

Yang

2019

PLoS ONE

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Workers processing nephrite, antigorite, or talc may be exposed to paragenetic asbestos minerals. An effective screening method for pneumoconiosis in workers exposed to asbestos-contaminated minerals is still lacking. The objective of this study was to assess the diagnostic accuracy of serum and urinary biomarkers for pneumoconiosis in workers exposed to asbestos-contaminated minerals. We conducted a case-control study in a cohort of stone craft workers in Hualien, where asbestos, nephrite, antigorite, and talc are produced. A total of 140 subjects were screened between March 2013 and July 2014. All subjects received a questionnaire survey and a health examination that included a physical examination; chest X-ray; and tests for standard pulmonary function, fractional exhaled nitric oxide, serum soluble mesothelin-related peptide (SMRP), fibulin-3, carcinoembryonic antigen (CEA), and urinary 8-Oxo-2'-deoxyguanosine (8-OHdG)/creatinine. After excluding subjects with uraemia and chronic obstructive pulmonary disease (COPD), we included 48 subjects with pneumoconiosis and 90 control subjects without pneumoconiosis for analysis. In terms of occupational history, 43/48 (90%) case subjects and 68% (61/90) of the control subjects had processed asbestos-contaminated minerals, including nephrite, antigorite, and talc. The case group had decreased pulmonary function in forced vital capacity (FVC), forced expiratory volume in one second, and forced expiratory flow between 25% and 75% of the FVC. The levels of SMRP, fibulin-3, urinary 8-OHdG/creatinine, and CEA were higher in the case group than in the control group. Subjects exposed to nephrite had significantly higher SMRP levels (0.84 ± 0.52 nM) than subjects exposed to other types of minerals (0.60 ± 0.30 nM). A dose-response relationship was observed between the SMRP level and the severity of pneumoconiosis. Machine learning algorithms, including variables of sex, age, SMRP, fibulin-3, CEA, and 8-OHdG/creatinine, can predict pneumoconiosis with high accuracy. The areas under the receiver operating characteristic curves ranged from 0.7 to 1.0. We suggest that SMRP and fibulin-3 could be used as biomarkers of pneumoconiosis in workers exposed to asbestos-contaminated minerals.

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Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension

Cited by 70 publications

References 132 publications

Machine learning prediction in cardiovascular diseases: a meta-analysis

Machine learning prediction in cardiovascular diseases: a meta-analysis

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals

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