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The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review
Nuryani et al [17] and Ghavidel et al [42] aim to predict and detect hypertension using convolution neural networks (CNN) and the need for cardiovascular surgery using deep neural networks, respectively. The first study achieved an F1-score of 98.88, while the second reached a recall of 72%, which may vary depending on the dataset used.
In contrast, Abbas et al [18] uses both ML and DL techniques to identify heart abnormalities.
JMIR Med Inform 2025;13:e64349
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The NER model can be easily used for information extraction without fine-tuning with the target documents since it is already fine-tuned with medical documents to detect symptoms.
In this study, we examined the usefulness of analyzing various medical Japanese documents, including medical records written by physicians and co-medical professionals, to capture the onset and duration of ADEs. Figure 1 shows the basic idea of our approach.
JMIR Med Inform 2024;12:e58977
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Despite its potential, the complexity of the heart rate signal, influenced by factors such as physical exercise [35] and stress responses [36], may complicate its ability to detect eating events. To accurately identify eating episodes, algorithms must account for these variables and differentiate between heart rate changes due to eating and other activities.
Interact J Med Res 2024;13:e52167
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“Notification! You May Have Cancer.” Could Smartphones and Wearables Help Detect Cancer Early?
Wearables are devices that can be worn to detect and monitor biometric data such as heart rate, blood oxygen saturation, sleep pattern, or temperature while the wearer continues their normal routines. A further category of wearables involves skin patches used to measure biochemical signals (ie, glucose) on a continuous basis that are increasingly being considered as a standard of care for individuals with certain conditions (eg, diabetes) [3].
JMIR Cancer 2024;10:e52577
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To detect symptoms, population health monitoring systems and research studies tend to largely rely on structured data from electronic health records, including the International Classification of Diseases, 10th Revision (ICD-10) codes [1]. However, symptoms are not diagnoses and, therefore, may not be consistently coded, leading to incorrect estimates of the prevalence of COVID-19 symptoms [2].
J Med Internet Res 2024;26:e53367
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A recent paper authored by the WHO underlines that being better prepared for future pandemics and epidemics will require increased collaboration among stakeholders and investment in collective abilities to detect and understand public health risks [3]. The use of Epi Core by multiple sectors can contribute toward furthering a multi-sectoral approach to epidemics and pandemics (ie, One Health Intelligence).
JMIR Public Health Surveill 2024;10:e52093
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EEG signals are collected over a period of time and analyzed to detect seizure events. Today, almost everyone uses smartphones, and smartphone apps are being used to solve real-world human challenges including health-related issues. Regarding the remote monitoring of patients with epilepsy, there is a need to develop an efficient smartphone app that processes long-term EEG recordings for seizure detection.
JMIR Neurotech 2023;2:e50660
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