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Some studies focus on simply identifying workload predictors rather than developing full predictive models [14], while others investigate alternative data collection methods, such as video-based workload estimation [15,16]. In addition, many studies classify current workload levels rather than predicting future nursing demands, making them less useful for proactive staff planning [17].
J Med Internet Res 2025;27:e66667
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This fixed spatial layout allows the model to process tabular information through CNN-based architectures, facilitating the use of transfer learning and potentially enhancing predictive performance. There has been growing interest in transforming structured tabular data into image representations to leverage the power of CNNs in domains traditionally dominated by machine learning.
JMIR Med Inform 2025;13:e75022
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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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