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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However, efforts to quantitatively evaluate fairness in prediction models for clinical practice are still scarce [15].
A model with high predictive accuracy does not guarantee the best clinical usage, as it might display unfavorable biases [16]. As a result, it is important to understand and quantify the trade-offs between accuracy and fairness in model selection.
JMIR Med Inform 2025;13:e66200
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We then incorporated a range of different observation periods and prediction windows (Figure 2) to test our prediction algorithms, considering the different use cases. We considered 4 different prediction windows: 0 years, 1 year, 3 years, and 5 years before CRC diagnosis.
Visualization of the observation and prediction windows. For the prediction task. The index date for CRC cases is the date of diagnosis.
JMIR Cancer 2025;11:e64506
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The reporting of the model development and validation followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD [37]) guidelines (Table S4 in Multimedia Appendix 1).
The strongest statistically significant unadjusted linear relationships between baseline predictors and mental health at follow-up are shown in Figure 4 and coefficients of all predictors can be found in Tables S1 and S2 in Multimedia Appendix 1.
JMIR Public Health Surveill 2025;11:e60125
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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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