scholarly journals Intelligent Internet of Things Medical Technology in Implantable Intravenous Infusion Port in Children with Malignant Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shaohong Liu ◽  
Luxing Jiang ◽  
Xin Wang
Keyword(s):  
Internet Of Things ◽  
Adverse Events ◽  
Healthcare System ◽  
Malignant Tumors ◽  
Three Dimensional ◽  
Smart Healthcare ◽  
Significant Efficacy ◽  
Hematological Tumors ◽  
Indoor And Outdoor

Due to the recent technological revolution that is centered around information technology, the Internet of Medical Things (IoMT) has become an important research domain. IoMT is a combination of Internet of Things (IoT), big data, cloud computing, ubiquitous network, and three-dimensional holographic technology, which is used to build a smart medical diagnosis and treatment system. Additionally, this system should automate various activities, such as the patient’s health record and health monitoring, which is an important issue in the development of modern and smart healthcare system. In this paper, we have thoroughly examined the role of a smart healthcare system architecture and other key supporting technologies in improving the health status of both indoor and outdoor patients. The proposed system has the capacity to investigate and predict (if feasible) the clinical application and nursing effects of totally implantable intravenous port (TIVAP) in pediatric hematological tumors. For this purpose, seventy children with hematologic tumors were treated with TIVAP, and IoMT-enabled care was provided to them, where the occurrence of adverse events, specifically after the treatment, was observed. The experimental results collected after the 70 children were treated and cared for by TIVAP show that there were five cases of adverse events, whereas the incidence rate of the adverse events was 7.14%. Moreover, TIVAP has significant efficacy in the treatment of hematologic tumors in children, and it equally reduces the vascular injury caused by chemotherapy in younger patients. Likewise, targeted care reduces the incidence of adverse events in children with expected ratio.

scholarly journals The Role of 3-Dimensional Power Doppler Imaging in the Assessment of Ovarian Teratoma in Pregnancy: A Case Report

2011 ◽  
Vol 2011 ◽  
pp. 1-4
Author(s):  
Konstantinos Kalmantis ◽  
Christos Iavazzo ◽  
Vasiliki Anastasiadou ◽  
Aris Antsaklis
Keyword(s):  
Malignant Tumors ◽  
Power Doppler ◽  
Three Dimensional ◽  
Preoperative Assessment ◽  
First Trimester ◽  
Doppler Imaging ◽  
Ovarian Teratoma ◽  
Maternal Complications ◽  
Diagnostic Modality

Background. Conventional sonography is the primary imaging tool for these pregnant women who present with an ovarian teratoma. In some cases, however, sonography diagnosis is difficult. We report a case of ovarian teratoma during pregnancy diagnosed by three-dimensional Power Doppler. The cyst was removed via laparotomy without fetal or maternal complications. Three-dimensional ultrasound with multiplanar view can better discriminate a benign ovarian teratoma from complex ovarian lesions or malignant tumors. Its role is significant especially during pregnancy as it may assist in determining which patients are requiring surgery and which are not. The results of three-dimensional sonography and magnetic resonance (MR) were equal but the role of MR imaging is limited in early pregnancy.Conclusions. Three-dimensional technique is a reliable diagnostic modality for preoperative assessment of an ovarian teratoma as it can be performed during the first trimester of pregnancy.

Role of Electronics Devices for E-Health in Smart Cities

2019 ◽  
pp. 212-233 ◽  
Author(s):  
Davinder Singh Rathee ◽  
Kiran Ahuja ◽  
Tadesse Hailu
Keyword(s):  
Internet Of Things ◽  
Health System ◽  
Health Information ◽  
Smart Cities ◽  
Computer Networking ◽  
Supporting System ◽  
Smart Healthcare ◽  
New System

As the world's population ages, those suffering from diseases will increase. Researchers in electronics, computer, networking, and medical fields need to work more seriously to make the broad vision of smart healthcare/e-health system. To achieve the objectives of e-healthcare in smart cities, there is a need to create new system that allows the acquisition of health information smartly, automatically, and transparently in order to take efficient decisions provided by the supporting system. Such systems may be designed technically by embedded together communication, smart signals, internet of things, network of sensors.

Stress Detection Using Machine Learning Classifiers in Internet of Things Environment

2019 ◽  
Vol 16 (10) ◽  
pp. 4214-4219
Author(s):  
Richa Sharma ◽  
Shalli Rani ◽  
Deepali Gupta
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Recommender Systems ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
Internet Of Things Environment ◽  
Novel Applications

Over the years, Recommender systems have emerged as a means to provide relevant content to the users, be it in the field of entertainment, social-network, health, education, travel, food or tourism. Further,with the expeditious development of Big Data and Internet of Things (IoT), technology has successfully associated with our everyday life activities with smart healthcare being one. The global acceptance towards smart watches, wearable devices or wearable biosensors have paved the way for the evolution of novel applications for personalized eHealth and mHealth technologies. The data gathered by wearables can further be interpreted using Machine learning algorithms and shared with healthcare experts to provide suitable recommendations. In this work, we study the role of recommender systems in IoT and Cloud and vice-versa. Further, we have analyzed the performance of different machine learning techniques on SWELL dataset. Based on the results, it is observed that 2 Class Neural network performs the best with 98% accuracy.

scholarly journals Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Benzhen Guo ◽  
Yanli Ma ◽  
Jingjing Yang ◽  
Zhihui Wang
Keyword(s):  
Deep Learning ◽  
Health Status ◽  
Internet Of Things ◽  
Healthcare System ◽  
Learning Model ◽  
Gray Scale ◽  
Data Set ◽  
Smart Healthcare ◽  
Cloud Server ◽  
Deep Learning Model

Introduction. Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward. Methods. Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set. Results. Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%. Conclusion. The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.

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