From Predictive Accuracy to Public Health Impact: Navigating the Challenges of Implementing a Hypertension Risk Model in Indonesia...Septian E, Khaefi MR, Athoillah A, et al. Prediction of Personalised Hypertension Using Machine Learning in Indonesian Population. Journal of Medical Systems. 2025;49(1):1-14.
The article discusses a study that developed machine learning (ML) models for predicting hypertension using data from Indonesia's SATUSEHAT platform. The study compared two models, one incorporating personal hypertension history and one without, highlighting the importance of model sensitivity and s...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 3 |
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| Autores principales: | , , |
| Formato: | commentary letter Journal Article |
| Publicado: |
Springer Nature
12/4/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189749686&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189749686 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 12/4/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189749686 189749686 189749686 10.1007/s10916-025-02313-w 189749686 ppf: 1 ppct: 2 formats: tig: atl: From Predictive Accuracy to Public Health Impact: Navigating the Challenges of Implementing a Hypertension Risk Model in Indonesia...Septian E, Khaefi MR, Athoillah A, et al. Prediction of Personalised Hypertension Using Machine Learning in Indonesian Population. Journal of Medical Systems. 2025;49(1):1-14. aug: au: Sheng, Tianqiang Liang, Zhiling Luo, Gangjian affil: https://ror.org/04tm3k558 Department of Anaesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, No. 600 Tianhe Road, 510630, Guangzhou, Guangdong Province, China sug: subj: Hypertension Risk Factors Risk Assessment Boosting Machine Learning Algorithms Utilization Prediction Models Resource-Limited Settings Early Diagnosis Noncommunicable Diseases Diagnosis Preventive Health Care Indonesia Public Health Implementation Science Research, Medical Health Care Costs ab: The article discusses a study that developed machine learning (ML) models for predicting hypertension using data from Indonesia's SATUSEHAT platform. The study compared two models, one incorporating personal hypertension history and one without, highlighting the importance of model sensitivity and specificity for effective population-wide screening. It raises concerns about data exclusion and potential biases, suggesting improvements in data handling and subgroup performance reporting. The authors emphasize the need for translating predictive model outcomes into actionable public health interventions, advocating for user-centered design and implementation frameworks to enhance real-world applicability and effectiveness. pubtype: Academic Journal doctype: commentary letter Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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