Analysis of Attack Patterns and Cyber Threats in Healthcare Sector

Information Technology (IT) has become the integral part of majority of businesses. Healthcare sector is also one such sector where IT adoption is increased in recent times. This adoption of IT has increased the internet exposure and hence increased the attack surface of the organisations working in healthcare sector. During covid outbreak, we have observed various cyber-attack and threats on organisations operating in healthcare sector. This paper focuses on cyber threat pattern in healthcare sector during covid-19 outbreak and post-covid-19 period. This research paper also aims to generate basic cyber awareness through generic cyber sanity checks to secure healthcare sector from malicious threat actors. The adaptation of proactive measures required to enhance the cyber hygiene of organisations becomes very essential in this sector. © 2023 IEEE.

ASSOCIATION RULE MINING FOR PREDICTION OF COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.

Optimizing Medical Healthcare Services Using Big Data Analytics

Big data is a very large size of datasets which come from many different sources and are in a wide variety of forms. Due to its enormous potential, big data has gained popularity in recent years. Big data enables us to investigate and reinvent numerous fields, including the healthcare industry, education, and others. Big data specifically in the healthcare sector comes from a variety of sources, including patient medical information, hospital records, findings from physical exams, and the outcomes of medical devices. Covid19 recently, one of the most neglected areas to concentrate on has come under scrutiny due to the pandemic: healthcare management. Patient duration of stay in a hospital is one crucial statistic to monitor and forecast if one wishes to increase the effectiveness of healthcare management in a hospital, even if there are many use cases for data science in healthcare management. At the time of admission, this metric aids hospitals in identifying patients who are at high Length of Stay namely LS risk (patients who will stay longer). Once identified, patients at high risk for LS can have their treatment plans improved to reduce LS and reduce the risk of infection in staff or visitors. Additionally, prior awareness of LS might help with planning logistics like room and bed allotment. The aim of the suggested system is to precisely anticipate the length of stay for each patient on an individual basis so that hospitals can use this knowledge for better functioning and resource allocation using data analytics. This would contribute to improving treatments and services. © 2023 IEEE.

The Use of Metaverse in the Healthcare Sector: Analysis and Applications

With the use of blockchain, Internet of Things, virtual platform/telecommunications network, artificial intelligence and the fourth industrial revolution, the essential demand for digital transition within the health care settings has increased as an outcome of the 2019 coronavirus illness outbreak and the fourth industrial revolution. The evolution of virtual environments with three-dimensional (3D) spaces and avatars, known as metaverse, has slowly gained acceptance in the field of health care. These environments may be especially useful for patient-facing platforms (such as platforms for telemedicine), functional uses (such as meeting management), digital education (such as modeled medical and surgical learning), treatments and diagnoses. This chapter offers the most recent state-of-the-art metaverse services and applications and a growing problem when it comes to using it in the healthcare sector. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluation of the Cross-Platform Framework Flutter Using the Example of a Cancer Counselling App

The advance of digitalization is constantly bringing new solutions to various areas of life in our society. The COVID-19 pandemic, among other things, brought increased attention to the application and support of treatments through digital solutions in the healthcare sector due to contact restrictions. However, the development of digital solutions comes at a high cost in terms of time and expenses. Mobile app development requires the development of two separate apps for the two respective market-leading mobile operating systems iOS and Android. Cross-platform frameworks make it possible to develop apps for both operating systems on a single code base, thus saving the development and maintenance of two separate codes. Flutter is currently the most popular cross-platform framework for the development of mobile apps. This paper has evaluated Flutter based on an existing criteria catalogue. As a usage context for the evaluation, a prototype for Cancer Counselling App of the University Medical Center Freiburg was implemented. According to the gained own prototyping experience with Flutter and a thorough literature analysis in this area, the criteria catalogue was filled out and the result was compared with other mobile App development paradigms. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Metaverse: An Innovative Model for Healthcare Domain

Present situation after the Coronavirus has made every one of us understand the deficiencies and the impediments of India's medical services area. There was an intense shortage of clinical staff, beds, and other such essential things, which made us believe this is the future to be lived with, and provided that this is true, then, at that point, it is a significant eye-opener for specialists, designers, government and each capable individual to think of an answer for this. This occasion touched off the inclination for the tracking down the arrangement or possibly a stage towards settling or, in any event, restricting this destruction. Metaverse, and its ground-breaking capacities are the same old thing to the world. It's been anticipated that it will revolutionize gaming, association with companions, shopping, and whatnot. But this paper is kept to spotlight the most deserving space, the healthcare sector. Metaverse can change the fortunes of the medical care area. This paper will examine all the potential ways this innovation can be valuable. It can work on obsolete facilities for treatment and educational purposes, and numerous such up-sides have been highlighted beneath. © 2023 IEEE.

Early Prediction of COVID-19 Infection with IoT and Machine Learning

The deadly virus COVID-19 has heavily impacted all countries and brought a dramatic loss of human life. It is an unprecedented scenario and poses an extreme challenge to the healthcare sector. The disruption to society and the economy is devastating, causing millions of people to live in poverty. Most citizens live in exceptional hardship and are exposed to the contagious virus while being vulnerable due to the inaccessibility of quality healthcare services. This study introduces ubiquitous computing as a state-of-The-Art method to mitigate the spread of COVID-19 and spare more ICU beds for those truly needed. Ubiquitous computing offers a great solution with the concept of being accessible anywhere and anytime. As COVID-19 is highly complicated and unpredictable, people infected with COVID-19 may be unaware and still live on with their life. This resulted in the spread of COVID-19 being uncontrollable. Therefore, it is essential to identify the COVID-19 infection early, not only because of the mitigation of spread but also for optimal treatment. This way, the concept of wearable sensors to collect health information and use it as an input to feed into machine learning to determine COVID-19 infection or COVID-19 status monitoring is introduced in this study. © 2023 IEEE.

Using machine learning to comprehend and forecast Post-COVID-19 pharmaceutical sales

The COVID-19 crisis has severely hampered the worldwide market, leading to several issues in the supply chain of several necessities, but a considerable increase in the healthcare sector for the pharmaceutical industry. Using machine learning, this research aims to comprehend and forecast pharmaceutical sector sales post-COVID-19. This paper analyzed the major non-communicable diseases and the pharmaceuticals used to treat them, discovered and determined the most significant factors, and utilized them to construct appropriate models for the study. An online survey was performed among Indian families using a structured questionnaire, including both open-ended and closed-ended questions on the family's health. Prior to and during the lockdown, information on non-communicable diseases and the usage of medications was gathered. Our results suggest that the unanticipated transformation in lifestyle has altered disease prevalence, which is a consideration for the pharmaceutical sector to address. And these models helped to figure out how disease levels were changing and how likely it was that the number of people with certain diseases would go up based on their symptoms. This gave a better idea of how to treat the patients. © 2023 IEEE.

Ontology-based semantic data interestingness using BERT models

The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

A Model to Detect Face Mask Using Deep Learning

Coronavirus pandemic started spreading in 2019 and is still spreading until now in 2021 all over the world. Due to this the healthcare sectors are going on crisis all over the world. One basic protective measure that we can implement in our daily life is wearing a face mask. Wearing a mask properly can control the spread of this virus to a great extent. Various regions have made wearing face mask mandatory to prevent spread of this virus. In this paper we have proposed a deep learning-based model to detect face mask using python, OpenCV, TensorFlow and it can be used in our health care sectors. © 2022 IEEE.

When Contactless Service Matters: The Use of Robotic Services in the Healthcare Sector

The Coronavirus disease 2019 (COVID-19) pandemic has led to a catastrophic public health emergency that impacted the global society's prosperity, health, and security. Concurrently, the swift technological development over the recent decades has enabled the rising implementation of robots in various industries. In particular, there is a growing demand for robotic technology in the healthcare sector as a precautionary measure since they significantly reduce the risk of cross-infection through interpersonal contact among medical professionals by shifting to computerized routine tasks. Therefore, this paper presents a comprehensive review of the use of robots in the healthcare sector during and post-COVID-19 pandemic. This paper highlights the increasing demand and adoption of robotic technology during and post-pandemic COVID-19 in healthcare sector. The benefits to the society and engineering managers, and challenges in implementing robotic technology in the healthcare sector are provided at the end of this paper before the paper ends with a concise conclusion. IEEE

Predicting Adverse Reaction of COVID-19 Vaccine with the Help of Machine Learning

The COVID-19 epidemic demonstrated the importance of technology in the healthcare sector. A lack of ventilators and essential drugs results in a high mortality rate. The most important lesson from the pandemic is that we must use all available resources to alleviate the situation during the pandemic. In this paper, we combine pharmacovigilance and machine learning to predict the effect of an adverse reaction on a patient. We take VAERS data and preprocess it before feeding it to various machine learning algorithms. We assess our model using various parameters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Health Monitoring System Using Robotics

The entire world witnessed the covid-19pandemicinthe year 2020. The actual outbreak of this corona virus was first reported in Wuhan, China and later declared to be epidemic by (WHO) World Health Organization. The whole world was under tremendous pressure in monitoring health, managing, and maintaining hospitals and inventing new drugs. Initially, India was very much worried because of the huge population. The pandemic posed a critical challenge for healthcare sectors, since doctors and nursing professionals were among the most severely affected and it's clear that India must adopt new measures to increase healthcare proportional ratio and adoption of new technologies to manage large population groups. Robotics is one area which may largely always support the segment. The proposed research project emphasized on developing robotic devices with robotic vision, sensors-based motion planning, dynamic obstacle detection, and autonomous navigation in a hospital environment and supported the medical and nursing teams in reducing their workload and improving patient health monitoring, also the research explored multi-robot exploration and integration. © 2022 IEEE.

CYBER SECURITY IN THE HEALTHCARE SECTOR - CURRENT THREATS

Information technology is a common part of human activities today. In most countries of the world, the security, health, and industrial sectors can be established here. The dependence of these sectors on information technology has been significantly strengthened by the global covid-19 pandemic and the current war in Ukraine. Data digitization has brought positives such as availability, fast transmission, and processing of necessary information. As well as negatives as constant attempts to steal, destroy or abuse them. Health care is one of the sectors where it is the target of not only enrichment attacks, but also terrorist motives - the attack causes chaos and panic among the population. The number of such attacks increased dramatically during the covid - 19 pandemic and the military conflict in Ukraine. The article is therefore intended to point out the need for constant evaluation of changing environmental influences and motivations for attacks with regard to the topicality of security. The introduction of the article contains a theoretical framework of the issue with a description of the motivation and behavior of the offender. It goes on to list the most common ways of conducting cyber-attacks. Significant attacks on hospitals in the Czech Republic and around the world are also mentioned. It focuses on their motivation, success and damage caused. Finally, the common characteristic strengths and weaknesses of the SWOT method are evaluated, followed by opportunities and threats. In the end, the current extended recommendations follow the current routine operation of security procedures with an emphasis on changes and new threats in the global environment. © 2022 International Multidisciplinary Scientific Geoconference. All rights reserved.

Autonomous Medical Assistive Robot for Medical Camp

The outbreak of the Covid-19 epidemic has devastated the generation and impacted multiple layers of the healthcare sector. Resulting from this kind of exceptionally contagious virus and a shortfall of medical workers in the hospitals, front-line health workers, and patients are at risk. Thus, with an aim to diminish the risk of infections, a mobile robotic system is proposed that can autonomously ensure safety and protection in the hospital. The system can monitor the patients by moving autonomously and sanitizing the floor throughout the hospital, which is implemented by Robot Operating System (ROS), SLAM (Simultaneous Localization and Mapping) algorithm, and A∗ search algorithm, and then it uses the MobileNetV2 algorithm for safety mask detection and giving voice alert. The system also offers AI voice communication to assist and diagnose the patients, which can lessen person-to-person contact. The system has anticipated 89% accuracy for AI custom dataset, whereas the validation accuracy for face mask detection is 95%. © 2022 IEEE.

Analyzing Wearable Data for Diagnosing COVID-19 Using Machine Learning Model

Health is an indispensable part of human life, but we realize its importance when we face health issues. Technology can play an important role in the healthcare sector. During the COVID-19 pandemic, many countries used technology to control the situation. Internet of Things-based wearable devices can change the whole scenario of diagnosing the disease. The physiological features collected using wearables can be used for pre-symptomatic prediction of disease. In this study, from the cohort of 185 participants, data of 36 participants are analyzed to predict COVID-19 before symptoms begin using the machine learning model. Our findings suggest that heart rate, BPM, SDNN, and steps features can be used to detect the COVID-19 before the symptoms appear. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Ecosystems, strategy, and resilience: Four empirical cases in the COVID-19 crisis

The pandemic pushed companies to rethink competitive strategies and the innovation ecosystem structure. Then, we studied four small organizations from different industries through interviews, observations, and documentation. We offer five key findings: First, the crisis affected the cases in distinct ways, benefiting those in the healthcare sector. Second, innovation ecosystems provided opportunities for digitalization to keep businesses running. Third, resilience and initiative from the ecosystem leaders were crucial in responding to the crisis. Fourth, shifts in competition and cooperation favored survival. Lastly, the competitive strategy and ecosystem goals set before the pandemic have not changed, but how to achieve them. © 2023 Elsevier B.V.

Detection of COVID-19 in High Resolution Computed Tomography Using Vision Transformer

In the current pandemic, precise and early diagnose of COVID-19 patient remained a crucial task for control of the spread of the COVID-19 virus in the healthcare sector. Due to the unexpected spike in COVID-19 cases, the majority of countries have experienced scarcity and poor testing rate. Chest X-rays and CT scans have been discussed in the literature as a viable source of testing for COVID-19 disease in patients. However, manually reviewing the CT and x-ray images is time-consuming and prone to error. Taking account into these constraints and the improvements in data science, this research proposed a Vision Transformer-based deep learning pipeline for COVID-19 diagnose from CT-based imaging. Due to the scarcity of large data sets, three open-source datasets of CT scans are pooled to generate 27370 images of covid and non- covid individuals. The proposed vision transformer-based model accurately diagnoses COVID-19 from normal chest CT images with an accuracy of 98 percent. This research would assist the practitioner, radiologist and doctors in early and accurate diagnose of COVID-19. © 2022 IEEE.

Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions

Recent years have witnessed increased pressure across the global healthcare system during the COVID-19 pandemic. The COVID-19 pandemic shattered existing healthcare operations and taught us the importance of a resilient and sustainable healthcare system. Digitisation, specifically adoption of Artificial Intelligence (AI) has positively contributed to developing a resilient healthcare system in recent past. To understand how AI contributes to building a resilient and sustainable healthcare system, this study based on systematic literature review of 89 articles extracted from Scopus and Web of Science databases is conducted. The study is organised around several key themes such as applications, benefits, and challenges of using AI technology in healthcare sector. It is observed that AI has wide applications in radiology, surgery, medical, research, and development of healthcare sector. Based on the analysis, a research framework is proposed using an extended Antecedents, Practices, and Outcomes (APO) framework. This framework comprises AI applications' antecedents, practices, and outcomes for building a resilient and sustainable healthcare system. Consequently, three propositions are drawn in this study. Furthermore, our study has adopted the theory, context and methodology (TCM) framework to provide future research directions, which can be used as a reference point for future studies. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Medical Assistance Robot with capabilities of Mask Detection with Automatic Sanitization and Social Distancing Detection/ Awareness

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated. © 2022 IEEE.