e.g. mhealth
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Skip search results from other journals and go to results- 4 Journal of Medical Internet Research
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Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review
Due to the complexity of schizophrenia, novel approaches are essential to better understand its neurobiological basis and improve outcome predictions, as it involves a network of genetic, neural, behavioral, and environmental factors [21].
Among novel approaches, machine learning has been increasingly used in the latest decade for various applications in medicine [22].
JMIR Bioinform Biotech 2024;5:e62752
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Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study
There is a broad range of PASC symptoms, diagnostic criteria, and hypothesized causal mechanisms, which has made it difficult for investigators to build generalizable predictions (Multimedia Appendix 1) [5-7]. Given this heterogeneity, multisite evaluations including large sample sizes and high-dimensional covariate information can provide opportunities to build models that can accurately predict PASC risk.
JMIR Public Health Surveill 2024;10:e53322
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Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review
Any data analytics that make inference or predictions from the data they receive were included in this review. This includes diagnosis, classification, and anomaly detection and does not exclusively consider predictions of future events. Additionally, this review only considers telecare and telehealth devices related to a somatic condition, that is, physical condition of the body.
Online J Public Health Inform 2024;16:e57618
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“It is difficult to make predictions, especially about the future” [1], as Yogi Berra stated.
J Med Internet Res 2024;26:e59826
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Different models, such as basic, statistical, machine learning (support vector regression [SVR], random forest, gradient boosting, etc), and deep learning (long short-term memory and gated recurrent unit) models, were compared to identify the best opportunity for obtaining high-precision predictions.
There are different learning models for regression and classification. Given the nature of the data sets and the continuous nature of the variables to be predicted, a regression learning model was used.
JMIR Form Res 2024;8:e54009
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This means that we are able to distinguish the reliability of our predictions for an individual with early versus late stage HF. However, as shown by Desai et al [48], adding electronic health record information to prediction of HF readmission in ML models did not improve model performance. Another limitation is the lack of cardiovascular imaging and measurement.
JMIR Cardio 2024;8:e54994
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On the other hand, prospective studies, which actively monitor outcomes against predictions, offer direct insights into AI model performance in real-world scenarios. They ensure better control over variables and reduce potential biases. A balanced approach that incorporates both retrospective and prospective designs is essential to fully harness the potential of AI in predicting and monitoring IVF outcomes.
Another important aspect to consider in AI models is bias in data selection and reporting.
J Med Internet Res 2024;26:e53396
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