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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study

A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study

We propose a Dynamic Adaptive Ensemble Learning Framework based on an Improved Harmony Search (DAELF-HSI), designed to enhance MCI detection by addressing issues of feature redundancy and the inefficiency of multimodal data fusion.

Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

JMIR Med Inform 2025;13:e60250

From the Public Health Perspective: a Scalable Model for Improving Epidemiological Testing Efficacy in Low- and Middle-Income Areas

From the Public Health Perspective: a Scalable Model for Improving Epidemiological Testing Efficacy in Low- and Middle-Income Areas

In disease detection systems in low- and middle-income areas, the usage and translational benefits of limited public health resources have a direct impact on the continuation of detection efforts. Load balancing is a quantitative assessment and optimization of resource interchange based on all available human, financial, material, and physical resources that the local area has.

Xuefeng Huang, Qian-Yi Kong, Xiaowen Wan, Yating Huang, Rongrong Wang, Xiaoxue Wang, Yingying Li, Yuqing Wu, Chongyuan Guan, Junyang Wang, Yuanyuan Zhang

JMIR Public Health Surveill 2024;10:e55194

Influence of Breast Cancer Awareness Month on Public Interest of Breast Cancer in High-Income Countries Between 2012 and 2022: Google Trends Analysis

Influence of Breast Cancer Awareness Month on Public Interest of Breast Cancer in High-Income Countries Between 2012 and 2022: Google Trends Analysis

This is partly due to better access to screening and health care, leading to earlier detection and treatment of the disease. In addition, lifestyle factors such as diet, physical activity, and alcohol consumption contribute to the incidence of breast cancer [7]. Therefore, breast cancer awareness is crucial in high-income countries given the high incidence rate, which can offer education and consequently potential for early detection and treatment [7].

Majed Ramadan, Doaa Aboalola, Sihem Aouabdi, Tariq Alghamdi, Mona Alsolami, Alaa Samkari, Rawiah Alsiary

JMIR Cancer 2024;10:e49197

Trial of the Pluslife SARS-CoV-2 Nucleic Acid Rapid Test Kit: Prospective Cohort Study

Trial of the Pluslife SARS-CoV-2 Nucleic Acid Rapid Test Kit: Prospective Cohort Study

For some low-resource primary health care conditions, the application scenarios of traditional nucleic acid testing are limited, and an assay with detection performance comparable to RT-q PCR and less demanding on the testing environment is needed. For high-traffic areas, such as customs and airports, the time required to obtain RT-q PCR results is too long, so a faster and more accurate test is needed to increase the speed of population screening.

Dandan Zhu, Jing Huang, Bei Hu, Donglin Cao, Dingqiang Chen, Xinqiang Song, Jialing Chen, Hao Zhou, Aiqun Cen, Tieying Hou

JMIR Public Health Surveill 2023;9:e48107

Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study

Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study

This study’s findings suggest that leveraging the power of Light GBM could enhance arrhythmia detection and monitoring, ultimately improving patient care and outcomes. In this review, we will talk about studies that introduce techniques for AF detection, analyzing their methods, results, and potential implications in the field of cardiology.

Beau Bo-Sheng Chuang, Albert C Yang

JMIR Form Res 2024;8:e47803

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Reference 29: eFurniture for home-based frailty detection using artificial neural networks and wireless Reference 33: Machine learning based walking aid detection in timed up-and-go test recordings of elderly Reference 36: Unobtrusive detection of frailty in older adultsdetection

Daniel Velazquez-Diaz, Juan E Arco, Andres Ortiz, Verónica Pérez-Cabezas, David Lucena-Anton, Jose A Moral-Munoz, Alejandro Galán-Mercant

J Med Internet Res 2023;25:e47346

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Results showed that the ML model outperformed every single clinician, yielding 0.79 (95% CI 0.62-0.92) accuracy, 0.75 (95% CI 0.46-1.0) sensitivity, and 0.83 (95% CI 0.58-1.0) specificity for COVID-19 detection. In this study, the performance was evaluated on 24 selected samples for the ML model as well as for each clinician, while these samples were chosen to be representative.

Jing Han, Marco Montagna, Andreas Grammenos, Tong Xia, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Ting Dang, Dimitris Spathis, R Andres Floto, Pietro Cicuta, Cecilia Mascolo

J Med Internet Res 2023;25:e44804

A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study

A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study

Reference 35: Development and validation of a deep learning algorithm for detection of diabetic retinopathy Reference 38: Automated and computer-assisted detection, classification, and diagnosis of diabetic retinopathy Reference 39: Detection of trachoma using machine learning approaches Reference 40: Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathydetection

Christopher J Brady, R Chase Cockrell, Lindsay R Aldrich, Meraf A Wolle, Sheila K West

J Med Internet Res 2023;25:e41233

Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

In response to the request for proposals, phys IQ collaborated with the NIH to deploy a method of collecting an immense volume of physiological data on patients with COVID-19 and develop a COVID-19 biomarker or index that could facilitate early detection of decompensation. In other words, the goal is to identify when an individual starts transitioning from being SARS-Co V-2–positive to having acute COVID-19.

Karen Larimer, Stephan Wegerich, Joel Splan, David Chestek, Heather Prendergast, Terry Vanden Hoek

JMIR Res Protoc 2021;10(5):e27271

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