The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were ran...To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were randomized to receive rapid-acting or sustained-release gliclazide therapy for 12 weeks.展开更多
In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of ...In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.展开更多
目的:分析重症患者抗癫痫药物(antiepileptic drugs,AEDs)用药情况、治疗药物监测(therapeutic drug monitoring,TDM)结果的影响因素,为重症患者AEDs临床合理用药提供参考。方法:系统回顾分析哈尔滨医科大学附属第一医院2019-2024年入...目的:分析重症患者抗癫痫药物(antiepileptic drugs,AEDs)用药情况、治疗药物监测(therapeutic drug monitoring,TDM)结果的影响因素,为重症患者AEDs临床合理用药提供参考。方法:系统回顾分析哈尔滨医科大学附属第一医院2019-2024年入住重症监护病房(intensive care unit,ICU)应用AEDs的559例患者的治疗信息,统计分析AEDs使用情况、TDM结果及影响因素。结果:ICU患者最常用的AED为丙戊酸钠(97.67%),其次为左乙拉西坦(20.57%);多数为单独用药(74.24%),且单用丙戊酸钠的比例最高(72.27%);最常用的联合方案为丙戊酸钠+左乙拉西坦;首次TDM达窗率较低(49.53%),剂量调整后达窗率显著提高至64.34%(P<0.05);给药方式、性别均可影响丙戊酸血药浓度(C)和血药浓度/剂量(C/D)(P<0.05),静脉给药,尤其是静脉持续给药后的C及C/D均高于非静脉给药(P<0.05)。结论:需强化ICU患者AEDs的TDM,重点关注给药方式及性别等因素对血药浓度的影响,依据TDM结果实施个体化用药,以提升治疗效果,降低不良反应。展开更多
The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe ...The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.展开更多
Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care,which is essential for independent living,especially as societies age and chronic diseases ...Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care,which is essential for independent living,especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common.Recent advances in the Internet of Things(IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition,gaining significant attention in personalized healthcare.This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring.Relevant papers were extracted and analyzed using a systematic numerical review method,covering various aspects such as sports monitoring,disease detection,patient monitoring,and medical diagnosis.The review highlights the transformative impact of IoTenabled wearable devices in healthcare,facilitating real-time monitoring of vital signs,including blood pressure,temperature,oxygen levels,and heart rate.Results from the reviewed papers demonstrate high accuracy and efficiency in predicting health conditions,improving sports performance,enhancing patient care,and diagnosing diseases.The integration of IoT in wearable healthcare devices enables remote patient monitoring,personalized care,and efficient data transmission,ultimately transcending traditional boundaries of healthcare and leading to better patient outcomes.展开更多
Considering the quality of life, manpower, and expenditure, an IoT-based health monitoring system has been proposed and implemented. Devices are placed on the human body to collect data, which is then uploaded to an o...Considering the quality of life, manpower, and expenditure, an IoT-based health monitoring system has been proposed and implemented. Devices are placed on the human body to collect data, which is then uploaded to an online data server. Specialist doctors can access this data as needed, allowing them to assess the patient’s initial condition and provide advice at any time. This approach enhances the quality and reach of health services. The module, designed and installed using modern technology, minimizes latency and maximizes data accuracy while reducing delay and battery drain. An accompanying app motivates public acceptance and ease of use. Various sensors, including ECG, SpO2, gyroscope, PIR, temperature-humidity, and BP, collect data processed by an Arduino microcontroller. Data transmission is handled by a WiFi module, with ThingSpeak and Google Sheets used for data processing and storage. The system has been fully tested, and patient data from two hospitals compared with the proposed model shows 97% accuracy.展开更多
IoT technology has emerged as a valuable tool in modern healthcare, providing real-time monitoring of patients, effective management of healthcare, and proper administration of patient information. The proposed system...IoT technology has emerged as a valuable tool in modern healthcare, providing real-time monitoring of patients, effective management of healthcare, and proper administration of patient information. The proposed system aims to develop a system that can prevent backward blood flow from stopping saline fluid, as well as monitor the temperature, heart rate, and oxygen level of patients by using multiple sensors like weight, temperature and heart rate, etc. Additionally, the proposed system can monitor the room temperature and humidity for contributing to the patient’s overall comfort. In emergency situations, it includes an emergency push button for quick alert medical staff and initiates timely interventions. It is designed to support nurses and doctors in monitoring patients and providing timely interventions to prevent complications.展开更多
患者生成型健康数据(patient-generated health data,PGHD)在急危重症早期识别与风险预警中扮演核心角色,但传统监测技术存在采集指标单一、精度不稳定等局限性,难以应对急危重症患者复杂、动态、多维度的监测需求。护理新材料凭借高灵...患者生成型健康数据(patient-generated health data,PGHD)在急危重症早期识别与风险预警中扮演核心角色,但传统监测技术存在采集指标单一、精度不稳定等局限性,难以应对急危重症患者复杂、动态、多维度的监测需求。护理新材料凭借高灵敏度、可穿戴性与智能响应特性,为PGHD的连续获取与实时分析提供新途径。该文综述了护理新材料在围手术期急症监测、慢性疾病急性加重识别及肿瘤急症预警中的应用进展,重点分析其在监测精度、灵敏度及与数字技术协同的机制,评估其在临床适应性、多源数据价值转化、患者数据主权保障等方面的现实挑战,探讨护理新材料由被动响应向智能预警转型的新思路,为急危重症护理监测的精准化、智能化研究提供参考。展开更多
Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous su...Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.展开更多
The present research intends to address in a comprehensive, transversal, and interdisciplinary manner the chronic patient management process in the research project named ＂PRO DOMO SUD＂ in order to identify operatio...The present research intends to address in a comprehensive, transversal, and interdisciplinary manner the chronic patient management process in the research project named ＂PRO DOMO SUD＂ in order to identify operational inefficiencies, thus demonstrating that these are largely attributable to incurred costs and, thus, evaluate possible solutions for providing effective and appropriate responses by healthcare and social services. Can patients/older people be treated, monitored, and managed successfully with mobile and wearable technologies？ The project involved three different groups of patients/participants： Patients with heart failure shock in ＂Home Monitoring Scenario＂; Patients with different pathologies in ＂Virtual Ward Scenario＂; Patients with limited mobility due to Neurological and Orthopaedic disease in ＂Rehabilitation Scenario＂. Due to the complexity of the issue, the methodological approach adopted must be multidimensional and interdisciplinary, addressing the complexity of the chronic patient from all viewpoints, not reducing it, yet analysing, understanding, rearranging, and managing it in an organic manner. The three different scenarios were allowed to identify several impacts on organizational and clinic management of chronic diseases, the tests showed significant improvements in quality of life of patients enrolled in the project. The data deriving from the three scenario demonstrate that wearable divide and ICT, in general, can empower both patients and physician personnel allowing them to be active part in the chronic disease management process. The PRO DOMO SUD experience derived from the Living Lab, this is a new paradigm for industrial research and development activities which allows the final users to actively collaborate with the designers and technicians in the development and test of new products and services aimed to them. The Living Labs stimulate social innovation by transferring research results from the closed industrial laboratory towards real life contexts where citizens and users become co-developers.展开更多
Mobile health applications, or mHealth apps, have gained popularity due to their practical functions and strengthening the connection between patients and healthcare professionals. These apps are designed for managing...Mobile health applications, or mHealth apps, have gained popularity due to their practical functions and strengthening the connection between patients and healthcare professionals. These apps are designed for managing health and well-being on portable devices, allowing individuals to self-manage their health or healthcare practitioners to enhance patient care. Key features include personalized recommendations, data synchronization with other health devices, and connectivity with healthcare professionals. The research describes how mobile health applications support healthy behaviors, facilitate communication between patients and physicians, and empower individuals in the United States to take charge of their health. This study also examines how adults in the US use mobile health applications, or mHealth apps, on their tablets or smartphones for health-seeking purposes. The information was taken from Cycle 4 of the Health Information National Trends Survey (HINTS 4). The challenges regarding these mobile health apps have also been evaluated with possible remedies. Around 100 university students participated in a cross-sectional study by answering questions on their eating habits, physical activity, lifestyle choices related to health, and use of mobile health apps. The data was then analyzed and concluded as a result. Mobile health applications have brought about a significant shift in the way patients connect with their healthcare providers by providing them with convenient access to health services and information. By keeping track of health markers like diet, exercise, and medication compliance, patients may use these tools to help better manage their chronic conditions. Mobile health applications can improve patient outcomes and save healthcare costs by empowering patients to take charge of their health. Through the facilitation of communication between patients and healthcare professionals, mobile health apps also offer virtual consultations and remote monitoring.展开更多
Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient c...Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures.Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures.This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.展开更多
目的开发急诊信息系统的心电监护报警参数自动计算与预警模块,并探讨其在急诊危重患者安全管理中的应用效果。方法成立危重患者心电监护报警参数设置的改进团队,设计研发急诊信息系统心电监护报警范围自动计算模块与预警模块。选取2023...目的开发急诊信息系统的心电监护报警参数自动计算与预警模块,并探讨其在急诊危重患者安全管理中的应用效果。方法成立危重患者心电监护报警参数设置的改进团队,设计研发急诊信息系统心电监护报警范围自动计算模块与预警模块。选取2023年3—12月浙江省某三甲医院急诊抢救室使用心电监护的危重患者作为研究对象,将报警范围自动计算与预警模块应用前后分为对照组(2023年3—5月)和观察组(2023年10—12月),比较2组患者心电监护报警参数设置正确率、报警设置错误同时期患者基本情况,观察实践效果。结果应用心电监护仪报警参数设置与预警模块后,心电监护报警参数设置正确率由75.53%提高至90.89%(χ2=400.57,P<0.01);应用心电监护仪报警参数设置及预警模块前后监护仪参数设置错误患者基本信息比较发现,模块应用后同时期护士管理急危重患者数明显增加[(5.66±0.41)例vs(7.98±0.67)例,t=-4.46,P<0.01],预检分诊Ⅰ、Ⅱ级危重患者参数设置错误例数明显减少(44例vs 26例,χ2=15.60,P<0.01);2组患者在入抢时改良早期预警评分(modified early warning score,MEWS)差异无统计学意义[(4.38±0.16)分vs(4.46±0.36)分,t=-0.20,P=0.84]。结论应用心电监护报警参数自动计算与预警模块能够提升急诊抢救室报警参数设置正确率,大大提升了护理工作效率,更好地保障护理安全,值得临床推广。展开更多
Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert p...Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.展开更多
基金supported by the National Science and Technology Council,Taiwan,with grant numbers NSTC 114-2622-8-992-007-TD1 and 112-2811-E-992-003-MY3.
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
文摘To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were randomized to receive rapid-acting or sustained-release gliclazide therapy for 12 weeks.
文摘In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.
文摘目的:分析重症患者抗癫痫药物(antiepileptic drugs,AEDs)用药情况、治疗药物监测(therapeutic drug monitoring,TDM)结果的影响因素,为重症患者AEDs临床合理用药提供参考。方法:系统回顾分析哈尔滨医科大学附属第一医院2019-2024年入住重症监护病房(intensive care unit,ICU)应用AEDs的559例患者的治疗信息,统计分析AEDs使用情况、TDM结果及影响因素。结果:ICU患者最常用的AED为丙戊酸钠(97.67%),其次为左乙拉西坦(20.57%);多数为单独用药(74.24%),且单用丙戊酸钠的比例最高(72.27%);最常用的联合方案为丙戊酸钠+左乙拉西坦;首次TDM达窗率较低(49.53%),剂量调整后达窗率显著提高至64.34%(P<0.05);给药方式、性别均可影响丙戊酸血药浓度(C)和血药浓度/剂量(C/D)(P<0.05),静脉给药,尤其是静脉持续给药后的C及C/D均高于非静脉给药(P<0.05)。结论:需强化ICU患者AEDs的TDM,重点关注给药方式及性别等因素对血药浓度的影响,依据TDM结果实施个体化用药,以提升治疗效果,降低不良反应。
文摘The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.
文摘Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care,which is essential for independent living,especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common.Recent advances in the Internet of Things(IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition,gaining significant attention in personalized healthcare.This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring.Relevant papers were extracted and analyzed using a systematic numerical review method,covering various aspects such as sports monitoring,disease detection,patient monitoring,and medical diagnosis.The review highlights the transformative impact of IoTenabled wearable devices in healthcare,facilitating real-time monitoring of vital signs,including blood pressure,temperature,oxygen levels,and heart rate.Results from the reviewed papers demonstrate high accuracy and efficiency in predicting health conditions,improving sports performance,enhancing patient care,and diagnosing diseases.The integration of IoT in wearable healthcare devices enables remote patient monitoring,personalized care,and efficient data transmission,ultimately transcending traditional boundaries of healthcare and leading to better patient outcomes.
文摘Considering the quality of life, manpower, and expenditure, an IoT-based health monitoring system has been proposed and implemented. Devices are placed on the human body to collect data, which is then uploaded to an online data server. Specialist doctors can access this data as needed, allowing them to assess the patient’s initial condition and provide advice at any time. This approach enhances the quality and reach of health services. The module, designed and installed using modern technology, minimizes latency and maximizes data accuracy while reducing delay and battery drain. An accompanying app motivates public acceptance and ease of use. Various sensors, including ECG, SpO2, gyroscope, PIR, temperature-humidity, and BP, collect data processed by an Arduino microcontroller. Data transmission is handled by a WiFi module, with ThingSpeak and Google Sheets used for data processing and storage. The system has been fully tested, and patient data from two hospitals compared with the proposed model shows 97% accuracy.
文摘IoT technology has emerged as a valuable tool in modern healthcare, providing real-time monitoring of patients, effective management of healthcare, and proper administration of patient information. The proposed system aims to develop a system that can prevent backward blood flow from stopping saline fluid, as well as monitor the temperature, heart rate, and oxygen level of patients by using multiple sensors like weight, temperature and heart rate, etc. Additionally, the proposed system can monitor the room temperature and humidity for contributing to the patient’s overall comfort. In emergency situations, it includes an emergency push button for quick alert medical staff and initiates timely interventions. It is designed to support nurses and doctors in monitoring patients and providing timely interventions to prevent complications.
文摘患者生成型健康数据(patient-generated health data,PGHD)在急危重症早期识别与风险预警中扮演核心角色,但传统监测技术存在采集指标单一、精度不稳定等局限性,难以应对急危重症患者复杂、动态、多维度的监测需求。护理新材料凭借高灵敏度、可穿戴性与智能响应特性,为PGHD的连续获取与实时分析提供新途径。该文综述了护理新材料在围手术期急症监测、慢性疾病急性加重识别及肿瘤急症预警中的应用进展,重点分析其在监测精度、灵敏度及与数字技术协同的机制,评估其在临床适应性、多源数据价值转化、患者数据主权保障等方面的现实挑战,探讨护理新材料由被动响应向智能预警转型的新思路,为急危重症护理监测的精准化、智能化研究提供参考。
文摘Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.
文摘The present research intends to address in a comprehensive, transversal, and interdisciplinary manner the chronic patient management process in the research project named ＂PRO DOMO SUD＂ in order to identify operational inefficiencies, thus demonstrating that these are largely attributable to incurred costs and, thus, evaluate possible solutions for providing effective and appropriate responses by healthcare and social services. Can patients/older people be treated, monitored, and managed successfully with mobile and wearable technologies？ The project involved three different groups of patients/participants： Patients with heart failure shock in ＂Home Monitoring Scenario＂; Patients with different pathologies in ＂Virtual Ward Scenario＂; Patients with limited mobility due to Neurological and Orthopaedic disease in ＂Rehabilitation Scenario＂. Due to the complexity of the issue, the methodological approach adopted must be multidimensional and interdisciplinary, addressing the complexity of the chronic patient from all viewpoints, not reducing it, yet analysing, understanding, rearranging, and managing it in an organic manner. The three different scenarios were allowed to identify several impacts on organizational and clinic management of chronic diseases, the tests showed significant improvements in quality of life of patients enrolled in the project. The data deriving from the three scenario demonstrate that wearable divide and ICT, in general, can empower both patients and physician personnel allowing them to be active part in the chronic disease management process. The PRO DOMO SUD experience derived from the Living Lab, this is a new paradigm for industrial research and development activities which allows the final users to actively collaborate with the designers and technicians in the development and test of new products and services aimed to them. The Living Labs stimulate social innovation by transferring research results from the closed industrial laboratory towards real life contexts where citizens and users become co-developers.
文摘Mobile health applications, or mHealth apps, have gained popularity due to their practical functions and strengthening the connection between patients and healthcare professionals. These apps are designed for managing health and well-being on portable devices, allowing individuals to self-manage their health or healthcare practitioners to enhance patient care. Key features include personalized recommendations, data synchronization with other health devices, and connectivity with healthcare professionals. The research describes how mobile health applications support healthy behaviors, facilitate communication between patients and physicians, and empower individuals in the United States to take charge of their health. This study also examines how adults in the US use mobile health applications, or mHealth apps, on their tablets or smartphones for health-seeking purposes. The information was taken from Cycle 4 of the Health Information National Trends Survey (HINTS 4). The challenges regarding these mobile health apps have also been evaluated with possible remedies. Around 100 university students participated in a cross-sectional study by answering questions on their eating habits, physical activity, lifestyle choices related to health, and use of mobile health apps. The data was then analyzed and concluded as a result. Mobile health applications have brought about a significant shift in the way patients connect with their healthcare providers by providing them with convenient access to health services and information. By keeping track of health markers like diet, exercise, and medication compliance, patients may use these tools to help better manage their chronic conditions. Mobile health applications can improve patient outcomes and save healthcare costs by empowering patients to take charge of their health. Through the facilitation of communication between patients and healthcare professionals, mobile health apps also offer virtual consultations and remote monitoring.
文摘Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures.Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures.This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.
文摘目的开发急诊信息系统的心电监护报警参数自动计算与预警模块,并探讨其在急诊危重患者安全管理中的应用效果。方法成立危重患者心电监护报警参数设置的改进团队,设计研发急诊信息系统心电监护报警范围自动计算模块与预警模块。选取2023年3—12月浙江省某三甲医院急诊抢救室使用心电监护的危重患者作为研究对象,将报警范围自动计算与预警模块应用前后分为对照组(2023年3—5月)和观察组(2023年10—12月),比较2组患者心电监护报警参数设置正确率、报警设置错误同时期患者基本情况,观察实践效果。结果应用心电监护仪报警参数设置与预警模块后,心电监护报警参数设置正确率由75.53%提高至90.89%(χ2=400.57,P<0.01);应用心电监护仪报警参数设置及预警模块前后监护仪参数设置错误患者基本信息比较发现,模块应用后同时期护士管理急危重患者数明显增加[(5.66±0.41)例vs(7.98±0.67)例,t=-4.46,P<0.01],预检分诊Ⅰ、Ⅱ级危重患者参数设置错误例数明显减少(44例vs 26例,χ2=15.60,P<0.01);2组患者在入抢时改良早期预警评分(modified early warning score,MEWS)差异无统计学意义[(4.38±0.16)分vs(4.46±0.36)分,t=-0.20,P=0.84]。结论应用心电监护报警参数自动计算与预警模块能够提升急诊抢救室报警参数设置正确率,大大提升了护理工作效率,更好地保障护理安全,值得临床推广。
基金supported by the Science Committee of the Ministry of Higher Education and Science of the Republic of Kazakhstan within the framework of grant AP23489899“Applying Deep Learning and Neuroimaging Methods for Brain Stroke Diagnosis”.
文摘Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.
