e.g. mhealth
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Skip search results from other journals and go to results- 10 Journal of Medical Internet Research
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Such techniques have been successfully applied in various health applications and predictive modeling [26-29].
This study aims to explore different ML techniques to develop a culturally informed, strengths-based AI model for predicting perinatal psychological distress in Aboriginal mothers. The model is built using holistic and culturally contextualized assessment data from the BCYR program.
J Med Internet Res 2025;27:e68030
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Current biological age prediction models, primarily based on conventional statistical methods such as multivariate regression analysis, rely on limited clinical data, restricting their predictive power and insights into the aging process [5-8]. Recent advances have led to models using omics data [9], including DNA methylation [10], transcriptome [11], metabolome [12], and telomere data [9].
JMIR Aging 2025;8:e64473
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The advancements in predictive analytics with deep learning methods have led to increased capabilities to extract meaningful information from medical images, including chest radiographs [23]. However, deep learning models have never been trained with chest radiographs to predict clinical deterioration outside the intensive care unit (ICU).
JMIR AI 2025;4:e67144
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Information about predictors of falls and strategies used to prevent falls in the RACFs was obtained by systematically reviewing published literature on predictive models for fall prevention, and the effectiveness of fall prevention interventions [15,16]. The identified predictors of falls in older people have been incorporated into the development of the embedded predictive model, which is reported elsewhere [17].
JMIR Aging 2025;8:e63609
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To further explain model performance, we also created model calibration plots and calculated secondary metrics of prediction models, including the confusion matrix and specificity, sensitivity, and predictive values.
There was only missing data in participants' age in the internal validation dataset (4/2228, 0.18%); therefore, a complete case analysis was performed on the dataset.
JMIR Aging 2025;8:e62942
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Predictive modeling is an efficient way to stratify patient readmission risk and optimize the allocation of clinical resources by providing preventive interventions to high-risk patients [3]. Traditional statistical methods for creating predictive models have focused on inference, which involves creating a mathematical model of a data-generating process to formalize an understanding of how it works or to test hypotheses.
JMIR Med Inform 2025;13:e56671
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Furthermore, current practices favor model evaluation metrics such as predictive accuracy without explaining how they are linked to a clinical decision. In the specific context of suicide risk detection, the authors advocate that prediction models should be compared to unstructured clinical assessments of suicide risk to investigate the incremental benefit of these tools in supporting clinician decision-making.
JMIR Form Res 2024;8:e63262
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Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review
Three out of the 7 tools employed predictive analytics. Methods included regression analysis and statistical modeling. Contemporary frameworks were also used for prediction and forecasting, including ML, deep learning, and simulation modeling [24,28].
Online J Public Health Inform 2024;16:e59906
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We aimed to (1) evaluate the current methods and criteria used to label patients with postoperative infections in prediction modeling and biomarker validation studies, (2) explore available automated surveillance methods and their performance (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) in comparison with reference standard manual chart review, and (3) determine the necessary data types and sources needed to perform automated detection of postoperative infections
JMIR Med Inform 2024;12:e57195
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