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Introduction: The detection and management of diseases have always been critical and challenging tasks for healthcare professionals. This necessitates expensive human and material resources, resulting in prolonged treatment processes. In medicine, misdiagnosis and mismanagement can significantly contribute to mistreatment and resource loss. However, machine learning (ML) techniques have demonstrated the potential to surpass standard patient treatment procedures, aiding healthcare professionals in better disease management. background: Machine learning (RFR) Method: In this project, the focus is on smart auscultation systems and resource management, employing Random Forest Regression (RFR). This system collects patients' physiological values (specifically, photoplethysmography techniques: PPG) as input and provides disease detection, treatment protocols, and staff assignments with greater precision. The aim is to enable early disease detection and shorten both staff and disease treatment durations. Result: Additionally, this system allows for a general diagnosis of the patient's condition, swiftly transitioning to a specific one if the initial auscultation detects a suspicious disease. Conclusion: Compared to the conventional system, it offers quicker diagnoses and satisfactory real-time patient sorting.
Introduction: The detection and management of diseases have always been critical and challenging tasks for healthcare professionals. This necessitates expensive human and material resources, resulting in prolonged treatment processes. In medicine, misdiagnosis and mismanagement can significantly contribute to mistreatment and resource loss. However, machine learning (ML) techniques have demonstrated the potential to surpass standard patient treatment procedures, aiding healthcare professionals in better disease management. background: Machine learning (RFR) Method: In this project, the focus is on smart auscultation systems and resource management, employing Random Forest Regression (RFR). This system collects patients' physiological values (specifically, photoplethysmography techniques: PPG) as input and provides disease detection, treatment protocols, and staff assignments with greater precision. The aim is to enable early disease detection and shorten both staff and disease treatment durations. Result: Additionally, this system allows for a general diagnosis of the patient's condition, swiftly transitioning to a specific one if the initial auscultation detects a suspicious disease. Conclusion: Compared to the conventional system, it offers quicker diagnoses and satisfactory real-time patient sorting.
BACKGROUND Information platforms have become a major avenue for public knowledge acquisition. However, the current framework of web platforms is not fully developed, and the needs of university students are not well understood by developers. Moreover, despite the low prevalence of HIV/AIDS in China, the situation remains critical for university student groups. OBJECTIVE The aim is to develop and establish a comprehensive, multi-module health education web platform tailored for university students. Specifically, to use HIV/AIDS prevention among university students as a case study to validate the effectiveness of the platform's educational modules. METHODS The web platform was developed following the behavioural guidelines of American medical and health websites. The modules were designed around “the three parts of a medical health education module course,” encompassing video learning, gaming, virtual community, and a question bank. Following development, experts were invited to test and refine the platform's modules. A preliminary experiment was conducted at the School of Nursing, Peking Union Medical College with nine participants, three from each grade. The platform was further refined based on these results. The study involved 146 non-medical undergraduate students from three different types of universities in Beijing, randomly divided into control (n=63) and intervention (n=63) groups. A web-based survey on Knowledge, Belief, and Practices (KBP) concerning AIDS, covering knowledge, belief, and high-risk behaviours with 36 items, was used. Baseline measurements for both groups were initially taken. The intervention group then used the web platform for three consecutive weeks, with a minimum usage of 120 minutes per week, sequentially accessing the video, gaming, and virtual community modules. The control group engaged in self-learning. Post-intervention, the total scores for knowledge, belief, and behaviour were measured for both groups. RESULTS The intervention group's baseline mean score was 22.34±1.668, and the control group's was 22.77±1.736, with no significant statistical difference in baseline data (P>0.05). After three weeks, the intervention group's average total score was 38.19±4.865, compared to 29.84±5.742 for the control group, indicating a statistically significant difference in AIDS KBP levels between the groups (P<0.05). CONCLUSIONS The web platform significantly positively impacted the AIDS KBP level among university students, suggesting that it has a significant positive effect on the health education of this demographic. It is recommended to continue developing modules for diseases beyond HIV/AIDS in the future. CLINICALTRIAL Approval:No.2022zglc06017
BACKGROUND Mobile health (mHealth) applications offer real-time monitoring and feedback, which can help in chronic disease management. However, their continuous operation drains device battery excessively, making long-term use challenging. To mitigate this, AI-driven techniques like adaptive sampling, task scheduling, and federated learning offer promising solutions for optimizing energy consumption while maintaining functionality. OBJECTIVE This study provides a systematic review focusing on AI-powered approaches for energy-efficient mHealth applications. It investigates methods such as adaptive sampling, task scheduling, and predictive behavior modeling for improved energy efficiency, enabling long-term battery-powered services without affecting monitoring. METHODS A systematic literature review of AI-based optimization techniques in mHealth was conducted, focusing on energy-saving characteristics of adaptive sampling and task scheduling. The analysis included 30 studies from 2016 to 2024, quantified by percentage improvements in energy savings and battery life. RESULTS Task scheduling achieved up to 40% energy savings, extending battery life by several hours. Adaptive sampling reduced energy consumption by 25-30%. Federated learning showed energy savings of up to 25% by minimizing data transmission. Predictive behavior modeling further optimized energy by adjusting resource use based on user interactions. CONCLUSIONS AI-driven techniques significantly reduce energy consumption in mHealth applications, particularly in chronic disease management. Task scheduling and adaptive sampling show the most promise for long-term monitoring without frequent recharging. Future research should explore advanced machine learning models and energy-harvesting technologies for more sustainable applications. CLINICALTRIAL None
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