e.g. mhealth
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Skip search results from other journals and go to results- 68 Journal of Medical Internet Research
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Prediction models that can identify infants with a high risk of ROP reactivation are needed in clinical practice.
Artificial intelligence has recently optimized medical practice [15-17]. Artificial intelligence has been mainly applied to ROP diagnosis and prediction based on imaging [17-19]. To our knowledge, studies on ROP reactivation after treatment are very limited, and there is no successful prediction model for clinical application.
J Med Internet Res 2025;27:e60367
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Uncertainty-aware AI models present the model’s uncertainty, or confidence in its decision, alongside its prediction [11], thus providing a metric for the user to assess the AI’s reliability [12]. CDSS reliability is an essential component of human evaluation of AI’s trustworthiness which determines the user’s acceptability of a technology [7].
JMIR Med Inform 2025;13:e64902
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However, clusters of evidence are emerging from the recent literature showing that levels of biomarkers of oxidative damage in biological fluids can be used for the prediction of measured concentrations of a limited number of exogenous and endogenous antioxidants [23].
Since March 2020, our health care and medical team began to hypothesize that antioxidant supplementation for patients in the intensive care unit (ICU) could improve their prognosis.
JMIR Form Res 2025;9:e66509
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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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Current EWS rely on structured data, such as vital signs and laboratory values, to predict clinical deterioration and ignore other data modalities that could potentially enhance prediction accuracy [7]. This results in lower detection and higher false-positive rates for these scores that could be mitigated by incorporating additional modalities [8].
JMIR AI 2025;4:e67144
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It is expected that such a prediction could help to achieve a more patient-specific therapeutic regime, tailored exercise support, and expectation management. However, currently, there is no evidence to support this hypothesis. This work takes an in-depth look at the telehealth data recorded during the Keep Pace study [20] by focusing on analyzing trends in patient-reported timeseries data.
JMIR Form Res 2025;9:e65721
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For the decision plotting that simulates the M-CHAT prediction pathway of each sample using SHAP, we specifically used samples with higher M-CHAT scores (M-CHAT scores 2‐4) within the M-CHAT positive group to focus on cases that might represent early developmental concerns.
Data analysis was performed using the open-source software R (version 4.1.2) and Python (version 3.8.5; Python Software Foundation). The correlation analysis results were visualized using Cytoscape (version 3.9.1; Cytoscape Consortium).
JMIR Pediatr Parent 2025;8:e58337
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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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