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Machine Learning Applied to Problem-Solving in Medical Applications

Computers, Materials & Continua 2021年第11期69卷 2277-2294页

作者： Mahmoud Ragab Ali Algarni Adel A.Bahaddad Romany F.Mansour Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicine King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Statistics Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Physical health plays an important role in overall well-being of the human *** is the most observed dimension of health among others such as social,intellectual,emotional,spiritual and environmental *** to exponential increase in the development of wireless communication techniques,Internet of Things(IoT)has effectively penetrated different aspects of human *** is one of the dynamic domains with ever-growing demands which can be met by IoT *** can be leveraged through several health service offerings such as remote health and monitoring services,aided living,personalized treatment,and so *** this scenario,Deep Learning(DL)models are employed in proficient disease *** current research work presents a new IoT-based physical health monitoring and management method using optimal Stacked Sparse Denoising Autoencoder(SSDA)technique i.e.,*** proposed model utilizes a set of IoT devices to collect the data from *** class problem poses serious challenges during disease diagnosis ***,the OSSDA model includes Synthetic Minority Over-Sampling Technique(SMOTE)to generate artificial minority class instances to balance the class ***,the hyperparameter settings of the OSSDA model exhibit heavy influence upon the classification performance of SSDA *** number of hidden layers,sparsity,and noise count are determined by Sailfish Optimizer(SFO).In order to validate the effectiveness and performance of the proposed OSSDA technique,a set of experiments was conducted on diabetes and heart disease *** simulation results portrayed a proficient diagnostic outcome from OSSDA technique over other *** proposed method achieved the highest accuracy values i.e.,0.9604 and 0.9548 on the applied heart disease and diabetes datasets respectively.

关键词： IoT patient monitoring physical health deep learning parameter tuning

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Automated patient Discomfort Detection Using Deep Learning

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Computers, Materials & Continua 2022年第5期71卷 2559-2577页

作者： Imran Ahmed Iqbal Khan Misbah Ahmad Awais Adnan Hanan Aljuaid Center of Excellence in Information Technology Institute of Management SciencesPeshawarPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)RiyadhSaudi Arabia

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been *** IoTbased sensors are used in the healthcare sector and offer quality and safe care to *** work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively *** do this,the overhead view patients’data set has been *** testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based *** model uses confidence maps and detects 18 different key points at various locations of the body of the *** association rules and part affinity fields,the detected key points are later converted into six main body ***,the distance of subsequent key points is measured using coordinates ***,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal *** accuracy of the proposed system is assessed on various test *** experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

关键词： Artificial intelligence patient monitoring discomfort detection deep learning

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A survey on the adoption of blockchain in IoT:challenges and solutions

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Blockchain(Research and Applications) 2021年第2期2卷 1-49页

作者： Md Ashraf Uddin Andrew Stranieri Iqbal Gondal Venki Balasubramanian Internet Commerce Security Laboratory Federation University AustraliaMount HelenBallaratVIC3350Australia

Conventional Internet of Things(IoT)ecosystems involve data streaming from sensors,through Fog devices to a centralized Cloud *** that arise include privacy concerns due to third party management of Cloud servers,single points of failure,a bottleneck in data flows and difficulties in regularly updating firmware for millions of smart devices from a point of security and maintenance *** technologies avoid trusted third parties and safeguard against a single point of failure and other *** has inspired researchers to investigate blockchain’s adoption into IoT *** this paper,recent state-of-the-arts advances in blockchain for IoT,blockchain for Cloud IoT and blockchain for Fog IoT in the context of eHealth,smart cities,intelligent transport and other applications are ***,research gaps and potential solutions are also presented.

关键词： Blockchain technology Consensus mechanism Blockchain cryptographic primitives healthcare patient monitoring Cloud of Things Internet of Things Fog of Things Software defined network Blockchain applications

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Design and Implementation of an IoT Based Remote Health monitoring System

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Journal of Computer and Communications 2024年第11期12卷 37-52页

作者： Taslim Arefin Abul Kalam Azad Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

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.

关键词： IoT patient monitoring MPU Sensors Accuracy Delay Power Consumption

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IoT Based Nurse Activities monitoring and Controlling System

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Advances in Internet of Things 2023年第3期13卷 63-82页

作者： Ahsan Ullah Md. Emtiaz Ahammed Md. Mohiuddin Bhuiyan Sourob Chandra Dasgupta Kazi Hassan Robin Nazmus Sakib Department of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh

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.

关键词： IoT Nursing Activities patient monitoring IV Saline Bag Arduino UNO NodeMCU (ESP8266) LM35 DHT11 MAX30102

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Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics

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World Journal of Critical Care Medicine 2017年第3期6卷 172-178页

作者： Jorg Peter Wilfried Klingert Kathrin Klingert Karolin Thiel Daniel Wulff Alfred Konigsrainer Wolfgang Rosenstiel Martin Schenk Department of Computer Engineering University of Tübingen Department of General Visceral and Transplant Surgery University Hospital Tübingen Department of Anesthesiology and Intensive Care Medicine University Hospital Tübingen

AIM To detect blood withdrawal for patients with arterial blood pressure monitoring to increase patient safety and provide better sample *** Blood pressure information obtained from a patient monitor was fed as a real-time data stream to an experimental medical framework. This framework was connected to an analytical application which observes changes in systolic, diastolic and mean pressure to determine anomalies in the continuous data stream. Detection was based on an increased mean blood pressure caused by the closing of the withdrawal three-way tap and an absence of systolic and diastolic measurements during this manipulation. For evaluation of the proposed algorithm, measured data from animal studies in healthy pigs were *** Using this novel approach for processing real-time measurement data of arterial pressure monitoring, the exact time of blood withdrawal could be successfully detected retrospectively and in real-time. The algorithm was able to detect 422 of 434(97%) blood withdrawals for blood gas analysis in the retrospective analysis of 7 study trials. Additionally, 64 sampling events for other procedures like laboratory and activated clotting time analyses were detected. The proposed algorithm achieved a sensitivity of 0.97, a precision of 0.96 and an F1 score of *** Arterial blood pressure monitoring data can be used toperform an accurate identification of individual blood samplings in order to reduce sample mix-ups and thereby increase patient safety.

关键词： Blood withdrawal detection Sample dating algorithm Arterial blood gas analysis patient monitoring Point-of-care diagnostics

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