Towards Smart Healthcare: Challenges and Opportunities in IoT and ML

The COVID-19 pandemic and other ongoing health crises have underscored the need for prompt healthcare services worldwide. The traditional healthcare system, centered around hospitals and clinics, has proven inadequate in the face of such challenges. Intelligent wearable devices, a key part of modern healthcare, leverage Internet of Things technology to collect extensive data related to the environment as well as psychological, behavioral, and physical health. However, managing the substantial data generated by these wearables and other IoT devices in healthcare poses a significant challenge, potentially impeding decision-making processes. Recent interest has grown in applying data analytics for extracting information, gaining insights, and making predictions. Additionally, machine learning, known for addressing various big data and networking challenges, has seen increased implementation to enhance IoT systems in healthcare. This chapter focuses exclusively on exploring the hurdles encountered when integrating ML methods into the IoT healthcare sector. It offers a comprehensive summary of current research challenges and potential opportunities, categorized into three scenarios: IoT-based, ML-based, and the implementation of machine learning methodologies in the IoT-based healthcare industry. This compilation will assist future researchers, healthcare professionals, and government agencies by offering valuable insights into recent smart healthcare advancements.Comment: 32 pages, 3 tables, 2 figures, chapter 10 revised version of "IoT and ML for Information Management: A Smart Healthcare Perspective" under "Springer Studies in Computational Challenge" serie

A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality

The Internet of Medical Things (IoMT) is a platform that combines Internet of Things (IoT) technology with medical applications, enabling the realization of precision medicine, intelligent healthcare, and telemedicine in the era of digitalization and intelligence. However, the IoMT faces various challenges, including sustainable power supply, human adaptability of sensors and the intelligence of sensors. In this study, we designed a robust and intelligent IoMT system through the synergistic integration of flexible wearable triboelectric sensors and deep learning-assisted data analytics. We embedded four triboelectric sensors into a wristband to detect and analyze limb movements in patients suffering from Parkinson's Disease (PD). By further integrating deep learning-assisted data analytics, we actualized an intelligent healthcare monitoring system for the surveillance and interaction of PD patients, which includes location/trajectory tracking, heart monitoring and identity recognition. This innovative approach enabled us to accurately capture and scrutinize the subtle movements and fine motor of PD patients, thus providing insightful feedback and comprehensive assessment of the patients conditions. This monitoring system is cost-effective, easily fabricated, highly sensitive, and intelligent, consequently underscores the immense potential of human body sensing technology in a Health 4.0 society

Towards a Domain – Specific Comparative Analysis of Data Mining Tools

Advancement in technology has brought in widespread adoption and utilization of data mining tools. Successful implementation of data mining requires a careful assessment of the various data mining tools. Although several works have compared data mining tools based on usability, opensource, integrated data mining tools for statistical analysis, big/small scale, and data visualization, none of them has suggested the tools for various industry-sectors. This paper attempts to provide a comparative study of various data mining tools based on popularity and usage among various industry-sectors such as business, education, and healthcare. The factors used in the comparison are performance and scalability, data access, data preparation, data exploration and visualization, advanced modeling capabilities, programming language, operating system, interfaces, ease of use, and price/license. The following popular data mining tools are assessed: SAS Enterprise Miner, KNIME, and R for business, Moodle Learning Analytics, Blackboard Analytics, and Canvas for education, and RapidMiner, IBM Watson Health, and Tableau for healthcare. It also discusses the critical issues and challenges associated with the adoption of data mining tools. Furthermore, it suggests possible solutions to help various industries choose the best data mining tool that covers their respective data mining requirements

An Adaptive Neuro-Fuzzy System with Semi-Supervised Learning as an Approach to Improving Data Classification: An Illustration of Bad Debt Recovery in Healthcare

Business analytics has become an increasingly important priority for organizations today as they strive to achieve greater competitiveness. As organizations adopt business practices that rely on complex, large-scale data, new challenges also emerge. A common situation in business analytics is concerned with appropriate and adequate methods for dealing with unlabeled data in classification. This study examines the effectiveness of a semi-supervised learning approach to classify unlabeled data to improve classification accuracy rates. The context for our study is healthcare. The healthcare costs in the U.S. have risen at an alarming rate over the last two decades. One of the causes for the rising costs could be attributed to medical bad debt, i.e., debt that is not recovered by healthcare institutions. A major obstacle to debt classification, hence better debt recovery, is the presence of unlabeled cases, a situation not uncommon in many other business contexts. There is surprisingly very little research that explores the performance of computational intelligence and soft computing methods in improving bad debt recovery in the healthcare industry. Using a real data set from a healthcare organization, we address this important research gap by examining the performance of an adaptive neuro-fuzzy inference system (ANFIS) with semi-supervised learning (SSL) in improving debt recovery rate. In particular, this study explores the role of ANFIS in conjunction with SSL in classifying unknown cases (those that were not pursued for debt collection) as either a good case (recoverable) or a bad case (unrecoverable). Healthcare institutions can then pursue these potentially good cases and improve their debt recovery rates. Test results show that ANFIS with SSL is a viable method. Our models generated better classification accuracy rates than those in prior studies. These results and their analysis show the potential of ANFIS with SSL models in classifying unknown cases, which are a potential source of revenue recovery for health care organizations. The significance of this research extends to all types of organizations that face an increasingly urgent need to adopt reliable practices for business analytics

Lstm Neural Networks and Iot Data for Predictive Maintenance in Healthcare

The most important in the modern provision of health care are medical devices that are involved in the process of prevention, diagnosis and treatment, rehabilitation. Ensuring their proper technical condition is the key to patient and user safety. However, the traditional ways of maintaining medical equipment are not enough for the increasing complexity of devices. By using information technology, social networking technologies, computerized systems digitization, and big data analytics, including machine learning, we have the ability to improve the quality of provision of services in the healthcare system. Predictive maintenance has become a fast-growing trend for assessing the technical condition of equipment and making predictions about possible failure scenarios to organize preventive maintenance. This systematic literature review will analyze previous research on predictive maintenance, with a special focus on its use in healthcare. The analysis of the articles found in several scientific search databases demonstrates that there is still much untapped potential for predictive maintenance in healthcare. This paper aims to introduce a new approach tuple, which will make it possible to provide proactive maintenance of medical equipment with the use of long short-term memory and Internet of things in healthcare analytics. This SLR will serve as a starting point to understand the predictive maintenance solutions in the industry, main findings, challenges, and new opportunities, and will give insights for future research regarding predictive maintenance

Unveiling the value of big data analytics use: A digital hospital case study

Big Data Analytics (BDA) has attracted significant attention from healthcare organisations seeking to leverage its analytical capabilities to resolve the challenges plaguing healthcare delivery. Yet, the outcomes of BDA have been inconclusive with unintended consequences arising, indicating the path for BDA value creation remains unclear. Responding to calls in literature, we seek to unveil the role of use in BDA value creation. Synthesising literature, we developed the BDA-Use-Value (BDA-UV) framework and subsequently used a case study of an Australian digital hospital to explore the framework. Overall, we found support for the BDA-UV framework and revealed nuanced insights surrounding a cyclical relationship between BDA use and system capabilities, a fuzzy boundary between BDA organisational capabilities and complementary organisational resources, and the complexity of BDA learning loop. Our framework and findings can also assist executives of digital hospitals to tailor their BDA strategies

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice

Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges. This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs. The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component. The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution