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 |
| Sumario: | 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. |
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