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The Impact Of Iomt In Remote Patient Monitoring And Healthcare Management
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Internet Of Medical Things (iomt) & Remote Patient Monitoring
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Internet Of Medical Things (iomt): What Is It, And How Can You Implement It In Your Healthcare Organization?
Written by Sujithra Thandapani Sujithra Thandapani Cilit Preprints.org Google Scholar 1, Muhammad Iqbal Mehboob Muhammad Iqbal Mehboob Scilit Preprints.org Google Scholar 2, Celestine Iwendi Celestine Iwendi Scilit Preprints.org Google Scholar 3, Muhammad Iqbal Mehboob Muhammad Iqbal Mehboob Scilit Preprints.org 4 , Angkor Dumka Angkor Dumka Scilit Preprints.org Google Scholar 5, 6, Mamoon Rashed Mamoon Rashed Scilit Preprints.org Google Scholar 7, 8, * and Senthilkumar Mohan Senthilkumar Mohan Scilit Preprints.org Google Scholar 9
Department of Computer Science and Engineering, Vel Tech Rangarajan, Dr. Sagunthala Research and Development Institute of Science and Technology, Chennai 600062, India
Received: 15 November 2022 / Revised: 30 December 2022 / Accepted: 10 January 2023 / Published: 13 January 2023
Understanding The Impact Of Iot (internet Of Things)in Healthcare
The Medical Internet of Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly focuses on the integration of medical supplies to serve needy people who cannot easily access medical services, especially rural residents and the elderly living alone. The main objective of this work is to design a real-time interactive system to provide medical services to the needy who do not have adequate medical infrastructure. With the help of this system, people will get medical services at their end with minimum medical infrastructure and less cost of treatment. However, a system designed to address the SARS family of viruses can be upgraded, and for the experiment, we took COVID-19 as a test case. The proposed system consists of many modules such as user interface, analytics, cloud, etc. The proposed interface is designed for interactive data collection. At the initial stage, it collects basic medical information such as pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they can get the pulse oxygen level. With the help of the exchange test kit, they can find a positive result for COVID-19. This information is uploaded as background information through an interface designed into the proposed system. If the system detects a positive case of coronavirus, it asks the person to upload X-ray/CT scan images to categorize the severity of the illness. The system is designed for multi-type data. Therefore, it can handle X-rays, CT images and text data (RT-PCR results). Once the X-ray/CT images are collected through the designed interface, those images are transferred to the designed AI module for analysis. The proposed artificial intelligence system is designed to classify many diseases. It categorizes patients with COVID-19, pneumonia, or any other viral infection. It also measures the level of severity of lung infection to provide appropriate treatment to patients. Several deep neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16 and VGG 19 for better classification. Through experiments, it was observed that ResNet101 and VGG 19 outperform for CT images with up to 97% accuracy. ResNet101 outperforms with up to 98% accuracy on X-ray images. To obtain enhanced accuracy, we used a basic voice classifier. It combines all the results of the classifiers and represents the majority with one vote. This leads to lower classifier bias. Finally, the proposed system provides an automated text summary report of the test. It can be accessed through an easy-to-use graphical user interface (GUI). This reduces reporting time and individual bias.
In late 2019, several unexplained cases of pneumonia were reported, which quickly spread across the country and around the world. This new, unknown virus has created many problems in the human body, such as acute breathing, multiple organ failure, cough, loss of smell, etc. [1, 2]. After a long search, the World Health Organization announced that the cause of pneumonia is infection with the new Corona virus. It was declared an international public health emergency by the World Health Organization in January 2020. Due to the rapid spread of the new coronavirus, more than 618 million people have been affected and 4.9 million people have died worldwide. If we have an adequate medical infrastructure for early screening, we can significantly reduce the death rate. Early detection of suspected patients plays a vital role in the prevention and control of novel coronavirus pneumonia cases. The arrival of the new Delta strain of Covid-19 worsens the situation, as transmission rates are significantly higher. Moreover, all countries are stuck on the choice of vaccine. The only way to reduce (and stop) the spread of the disease is to correctly diagnose infected individuals. Many medical and technical examinations were performed for accurate diagnosis. Finally, the medical community announced an RT-PCR test to test positive for the coronavirus. Reverse transcription polymerase chain reaction (RT-PCR) is used to determine the positivity of the coronavirus. Although RT-PCR is considered a standard diagnostic tool by the World Health Organization (WHO), its accuracy is only 80%. The difficulty with RT-PCR is that it gives a negative result if it is performed after the end of the life of the Covid-19 virus. In such cases, CT and X-ray imaging are usually used along with RT-PCR to determine the severity (stage) of the disease and the treatment plan. Some of the challenges to achieving accurate and timely treatments are (1) the interpretation of infection by the radiologist is a highly subjective endeavor that is always subject to individual bias and therapeutic practice. (2) Lack of medical infrastructure, especially in rural areas, prevents patients from receiving timely care. In this regard, a lot of technical research is being done in deep learning methods for prediction and classification of pneumonia, viral infection and COVID-19. They took CT scans and X-rays to train and test their system. Researchers have tried many artificial intelligence techniques to improve image classification. Although AI techniques, especially neural network architecture, provide better image classification and prediction, they need a huge volume of images to get better results. In the proposed system, we used a deep convolutional neural network architecture for image classification. The challenge we faced was data collection. Since the collected data is not sufficient to obtain better accuracy, we used the Keras image data generator to generate synthetic data. With the help of data augmentation, we have created sufficient data. It helps to get better ranking results. Some researchers work on the development of diseases and their consequences. In , the authors proposed a logical framework to study the development of neurological diseases using answer set programming (ASP). They conducted a research study on ANN and ASP for the development of neurological diseases. From the experiment, it was observed that the combination of ANN and ASP helped to study the neural network and check the effect of their differences on neurological diseases. However, deep convolutional neural network architectures provide better accuracy for image classification. Some researchers are working on convolutional quantum neural networks for better classification. To further improve the accuracy of convolutional neural network architectures, several researchers have recently worked [4, 5, 6, 7] on integrating quantum computing with neural network architectures, yielding high accuracy, especially for image classification. Through the study, it was noted that all developed systems are treatment support systems. It will benefit the medical community in treating patients and simplifying their tasks. It cannot directly address the problems of society. We have implemented an automated system to help people infected with the coronavirus (COVID-19). Because it can achieve quick results, we can significantly stop the spread rate. This results in a low death rate with minimal medical infrastructure. The Internet of Things (IoT) is a field of automation. As automation delivers precise results, every industry from retail to satellite communications is moving towards automation. It becomes an integral part of every walk
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