Intelligent Healthcare System Research Articles

Flexible pressure sensor based on thermoplastic polyurethane/polyvinylidene fluoride nanofiber membranes

The mechanical flexibility of flexible sensors primarily originates from sensitive flexible materials. Therefore, the key to developing new flexible mechanical sensors lies in constructing high-performance sensitive flexible materials. In this study, we employ a coaxial electrostatic spinning technique to prepare thermoplastic polyurethane (TPU)/polyvinylidene fluoride (PVDF) nanofiber membranes (NFM) with a core–shell structure for the construction of flexible piezoelectric mechanosensors. The utilization of TPU/PVDF NFM in flexible mechanosensors for human health monitoring and environmental sensing is investigated. As a result, the excellent performance of this composite membrane was demonstrated by its high mechanical strength and flexibility, resulting in high sensitivity (GF: 0.42 V/kPa), low response time (100 ms), and wide sensing range (0.2∼5.0 kPa). Additionally, The TPU/PVDF NFM not only demonstrates excellent pressure sensing performance but also exhibits exceptional capabilities for human body sensing applications. Therefore, these results highlight that the TPU/PVDF NFM exhibits excellent pressure-sensing properties and holds significant potential for applications in the fields of flexible robotics and wearable devices such as human-computer interfaces, intelligent healthcare systems, and artificial intelligence skin technologies.

A Framework Design for Centralised Monitoring of Patient Disease Diagnosis for Better Improvement

Healthcare recommendation systems have garnered significant attention in recent times due to their capacity to improve patient outcomes and treatment. This literature review intends to assess the current state of patient healthcare referral systems by examining relevant studies, techniques, and findings. The report focuses on key research areas, challenges, and viable strategies for the future in the field of patient-centered health recommendation systems. Currently, healthcare administration is in high demand due to its significant advantages in managing hospitals or medical practices. Health management systems are increasingly affecting the entire world on a daily basis. The rising demand for healthcare is attributed to various factors, including the availability of healthcare solutions. The health prediction system is an online initiative designed to provide user support and advice. This study proposes a technology that allows consumers to receive immediate online health guidance from an intelligent healthcare system. The system encompasses a multitude of disorders and symptoms associated with different bodily systems. Data mining technologies can be utilized to identify the most probable disease associated with a patient's symptoms. By logging into the system, a doctor can retrieve and review their patient's information and reports within the doctor's module. Physicians have the ability to analyze the patient's browsing history and the specific information they are seeking, taking into account their medical prognosis. The doctor has access to his data. The database administrator has the ability to incorporate additional disease information, such as the type of disease and its symptoms. The data mining system runs based on the condition's name and symptoms. The administrator has access to the database including information on diseases and symptoms. Recommender systems employ diverse machine learning techniques in many domains, such as the healthcare recommendation system (HRS), to advise and promote services or entities to users. Due to the vast array of algorithms documented in the literature, the science of artificial intelligence is now widely employing machine learning techniques in various application domains, including the HRS. Nevertheless, the process of selecting an appropriate machine learning algorithm for a health recommender system seems to be time-consuming.

Future of Medical Equipment Technology

Advances in manufacturing and digital information technology have sparked the Industrial Revolution 4.0. UDIs and 6G telemedicine networks are reshaping the future of healthcare technology as part of the Fourth Industrial Revolution (4IR), which is now underway. With the help of UDIs, device recalls and post-market surveillance may be done more accurately and efficiently, and studies using UDI data can be conducted to help doctors make better clinical decisions. The development of intelligent healthcare systems is currently a hot topic, and wireless connectivity is a critical component. The goal of the 6G vision is to transform the globe into a secure and completely linked digital society through the use of communication technologies with intelligence. Stentrode, digital pacemaker, insulin pumps and coronary intervention with drug-eluting stents are some of the new methods that will improve human life. As technology continues to evolve, so will our methods of self-care. In the future, patients will continue to seek out companies that use technological innovation in medicine to empower them to take charge of their own health. The healthcare system is evolving to become more proactive, personalised, and easy for you, which makes it easier than ever to live longer and perform better.

Voronoi diagrams and tree structures in HRP-EE: Enhancing IoT network lifespan with WSNs

Within the domain of the Internet of Things (IoT), wireless sensor networks (WSNs) play a pivotal role, facilitating advancements in sectors such as smart urban infrastructures, intelligent healthcare systems, and residential automation. Despite their versatility, WSNs grapple with challenges like limited energy reserves and constrained computational capacities, making energy conservation a paramount concern for IoT deployments underpinned by WSNs. This study introduces the Hybrid Routing Protocol for Efficient Energy (HRP-EE) designed to enhance the operational efficiency of WSNs. The HRP-EE protocol unfolds over three meticulous stages: the selection of cluster heads, the formation of clusters, and the establishment of routing pathways. Initially, the protocol evaluates nodes based on their residual energy and their Euclidean distance to the central sink (or gateway) device to designate apt cluster heads. Subsequently, these chosen cluster heads are integrated into the Voronoi diagram as central nodes to orchestrate cluster architectures. In the terminal stage, an innovative hybrid algorithm is instituted. This algorithm amalgamates the principles of the Minimum Spanning Tree for structuring intra-cluster communication trees and Dijkstra’s algorithm to ascertain the most efficient paths for inter-cluster data transmission from cluster heads to the sink device. The primary objective of this protocol is the judicious utilization of the sensor nodes’ energy, thereby optimizing the overall network longevity. To assess the proficiency of HRP-EE, we executed a series of simulations using NS2. Comparative analyses reveal that HRP-EE outperforms existing protocols such as LEACH-VA, PEGCP, and TBC, delivering superior energy efficiency, extended network lifespan, and enhanced throughput across both homogeneous and heterogeneous network architectures.

A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless Body Area Networks

So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve a single application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic type and the fulfillment of application requirements, such as reliability and timeliness. Nevertheless, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium Access Control (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.

Industrially Scalable Textile Sensing Interfaces for Extended Artificial Tactile and Human Motion Monitoring without Compromising Comfort.

Smart wearables with the capability for continuous monitoring, perceiving, and understanding human tactile and motion signals, while ensuring comfort, are highly sought after for intelligent healthcare and smart life systems. However, concurrently achieving high-performance tactile sensing, long-lasting wearing comfort, and industrialized fabrication by a low-cost strategy remains a great challenge. This is primarily due to critical research gaps in novel textile structure design for seamless integration with sensing elements. Here, an all-in-one biaxial insertion knit architecture is reported to topologically integrate sensing units within double-knit loops for the fabrication of a large-scale tactile sensing textile by using low-cost industrial manufacturing routes. High sensitivity, stability, and low hysteresis of arrayed sensing units are achieved through engineering of fractal structures of hierarchically patterned piezoresistive yarns via blistering and twisting processing. The as-prepared tactile sensing textiles show desirable sensing performance and robust mechanical property, while ensuring excellent conformability, tailorability, breathability (288 mm s-1), and moisture permeability (3591 g m-2 per day) for minimizing the effect on wearing comfort. The multifunctional applications of tactile sensing textiles are demonstrated in continuously monitoring human motions, tactile interactions with the environment, and recognizing biometric gait. Moreover, we also demonstrate that machine learning-assisted sensing textiles can accurately predict body postures, which holds great promise in advancing the development of personalized healthcare robotics, prosthetics, and intelligent interaction devices.

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

Aims and Background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Objectives and Methodology: However, the Internet-of-Things(IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results and Conclusions: This study also contributes in two ways to the ongoing effort to investigate security-privacy concerns in the circumstance of smart cities for healthcare purposes. Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented.

Analysis of System of Treatment Opted for Diabetes and Hypertension During Covid-19

Background: India has two healthcare components, 1stas public which focus on basic healthcare facilities in the form of Ayushman Bharat-Primary Healthcare Centres (AB-PHCs) and Ayushman Bharat-Health Sub-Centres (AB-HSCs) in rural areas and comprises limited secondary and tertiary care institutions in key cities whereas 2nd as private sector which provides majority of secondary, tertiary, and quaternary care institutions with major concentration in metros, tier-I and tier-II cities. Indian Healthcare Sector has continued to grow at a significant rate through effective budgetary allocations and promoting collaborations. Centre and State governments spend 30% and 70% of total government spending on health care, respectively. As about 35 million diabetic and hypertension patients of aged ≥65 years are susceptible to mortality due to COVID-19, due to effect on heart, liver, kidneys and lungs, this needs more concentration and proper management of these patients. There is requirement to build an intelligent healthcare system to span the public vs private and conventional vs non-conventional healthcare system, aligned with international principles and standards for health metrics. The global prevalence of these NCDs is higher in urban than rural areas and higher in high-income than low-income countries. One in two people living with these NCDs do not know that they have these diseases. Economy of the individual and country have affected negatively by diabetes and hypertension patients. 80% of the burden of these diseases of a country can be reduced by appropriate medicines. Due to no or less side-effects, many modern medical practitioners are practicing the non-conventional system of treatment which is 80% in rural areas. Objective: To study the system of treatment opted by the diabetic and hypertension patients during COVID-19. Material and Method: A cross-sectional descriptive study is conducted at various institutional levels and by visiting randomly in different selected areas with help of well -developed questionnaire. The research work is conducted by collecting the data of diabetic and hypertension patients with their system of treatment opted during COVID-19 in the years 2020, 2021, 2022 and 2023. Sample size is between 900 to 1000 participants and total investigations done, are between 5300 to 5500. The investigations are done with the help of glucometer and sphygmomanometer. Results: Total persons investigated are 5498, normal persons are 82.78% whereas diseased persons are 17.22% out of these diseased persons HTN are 10.53%, DM are 3.87% and HTN with DM are 2.82%. From 947 DM, HTN and HTN with DM patients, modern allopathic medicines or conventional system users are, DM=152 (16.05%) HTN = 316 (33.37%), DM with HTN = 103 (10.88%). Traditional medicines or non- conventional system users are DM=26 (2.75%), HTN= 106 (11.19%), DM with HTN = 24 (2.53%). Both healthcare system user are, DM=35 (3.69%), HTN=157 (16.58%), DM with HTN =28 (2.96%). Total patients of DM, HTN and HTN with DM which used modern allopathic medicines or conventional healthcare system are 571 (60.30%), traditional medicines or non-conventional healthcare system are 156 (16.47%) and which have used both conventional as well as non-conventional system are 220 (23.23%). Conclusions: More patients of DM, HTN and HTN with DM have adopted modern allopathic medicines/ conventional system for treatment as compare to traditional/ non-conventional system whereas many of these patients have adopted both systems of treatment conventional as well non-conventional.

An Intelligent Health Care System for Detecting Drug Abuse in Social Media Platforms Based on Low Resource Language

An Intelligent Health Care System for Detecting Drug Abuse in Social Media Platforms Based on Low Resource Language

A Blockchain-Based Hybrid Model for IoMT-Enabled Intelligent Healthcare System

A Blockchain-Based Hybrid Model for IoMT-Enabled Intelligent Healthcare System

An AoI-Aware Data Transmission Algorithm in Blockchain-Based Intelligent Healthcare Systems

An AoI-Aware Data Transmission Algorithm in Blockchain-Based Intelligent Healthcare Systems

Features of Classification of Crimes Committed by Persons using Artificial Intelligence Technologies in Healthcare

Recognizing positive possibilities of artificial intelligence technologies in healthcare, as well as current ways to use them, the author identifies the main forms of implementation of digital innovation: physical form in the form of a medical robot and intellectual form in the form of software, registered as medical devices. It is stated that the legal issues related to bringing to justice for actions related to the use of intelligent systems in healthcare, which led to negative consequences, including harm to the life and health of patients, have yet to be resolved. According to the current legal regulation in Russia it is a medical organization and a medical professional using artificial intelligence systems or medical robotics equipped with digital technologies who are held liable for the harm caused to the life and (or) health of citizens while providing them with medical care. In turn, system developers, as well as those who train a system based on artificial intelligence (developers of artificial intelligence systems), are not held liable. The problems of classification of crimes committed by medical professionals using artificial intelligence technologies in healthcare are considered. A medical worker providing medical care using artificial intelligence may be the subject of a crime under Part 2 of Article 109 and part 2 of Article 118 of the Criminal Code of the Russian Federation, but not under Article 238 of the Criminal Code of the Russian Federation. In addition, the rules for the classification of crimes committed by other entities (the operator of information systems) using artificial intelligence technologies are formulated.

Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues

Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues

Optimized Hybrid Deep Learning for Real-Time Pandemic Data Forecasting: Long and Short-Term Perspectives

Background:: With new variants of COVID-19 causing challenges, we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis. Objective:: This article suggests three hybrid deep learning models, namely CNN-LSTM, CNN-Bi- LSTM, and CNN-GRU, to address the pressing need for an intelligent healthcare system. These models are designed to capture spatial and temporal patterns in COVID-19 data, thereby improving the accuracy and timeliness of predictions. An output forecasting framework integrates these models, and an optimization algorithm automatically selects the hyperparameters for the 13 baselines and the three proposed hybrid models. Methods:: Real-time time series data from the five most affected countries were used to test the effectiveness of the proposed models. Baseline models were compared, and optimization algorithms were employed to improve forecasting capabilities. Results:: CNN-GRU and CNN-LSTM are the top short- and long-term forecasting models. CNNGRU had the best performance with the lowest SMAPE and MAPE values for long-term forecasting in India at 3.07% and 3.17%, respectively, and impressive results for short-term forecasting with SMAPE and MAPE values of 1.46% and 1.47%. Conclusion:: Hybrid deep learning models, like CNN-GRU, can aid in early COVID-19 assessment and diagnosis. They detect patterns in data for effective governmental strategies and forecasting. This helps manage and mitigate the pandemic faster and more accurately.

Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases

Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors.

KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning

BackgroundsPredicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes. Few studies consider the valuable relations, including synergistic relations between medications, concurrent relations between diseases, and therapeutic relations between medications and diseases from historical EMR. This limitation restricts prediction performance and application scenarios.MethodsTo address these limitations, we propose KAMPNet, a multi-sourced medical knowledge augmented medication prediction network. KAMPNet captures diverse relations between medical codes using a multi-level graph contrastive learning framework. Firstly, unsupervised graph contrastive learning with a graph attention network encoder captures implicit relations within homogeneous medical codes from the medical ontology graph, generating knowledge augmented medical code embedding vectors. Then, unsupervised graph contrastive learning with a weighted graph convolutional network encoder captures correlative relations between homogeneous or heterogeneous medical codes from the constructed medical codes relation graph, producing relation augmented medical code embedding vectors. Finally, the augmented medical code embedding vectors, along with supervised medical code embedding vectors, are fed into a sequential learning network to capture temporal relations of medical codes and predict medications for patients.ResultsExperimental results on the public MIMIC-III dataset demonstrate the superior performance of our KAMPNet model over several baseline models, as measured by Jaccard, F1 score, and PR-AUC for medication prediction.ConclusionsOur KAMPNet model can effectively capture the valuable relations between medical codes inherent in multi-sourced medical knowledge using the proposed multi-level graph contrastive learning framework. Moreover, The multi-channel sequence learning network facilitates capturing temporal relations between medical codes, enabling comprehensive patient representations for downstream tasks such as medication prediction.

Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture.

Blockchain-Powered Tensor Meta-Learning-Driven Intelligent Healthcare System With IoT Assistance

The rapid development and gradual integration of artificial intelligence and the Internet of Things have brought unprecedented opportunities for radically changing healthcare and treatments. However, the burgeoning in intelligent healthcare systems is severely bounded by data privacy and the security of AI models. Meanwhile, the limited local data forces conventional AI models to face the predicament in achieving personalized healthcare. Hence, we propose a blockchain-powered tensor meta-learning-driven intelligent healthcare system with IoT assistance. IoT devices as light nodes upload the local shareable data to the edge server(full node) for model training and perform the local private data by non-tampered model downloaded via smart contract. The system can not only use blockchain technology to ensure the strong consistency of the healthcare model but also protect private data from being leaked. Especially, we develop a tensor meta-learning model named tensor-prototype graph network to achieve efficient modeling of heterogeneous healthcare data. Building on the tensors and graph network, the model is conducive to capturing the data distribution when there are few labeled data. To evaluate our proposed approach, we have conducted experiments on three classic databases. The results demonstrate that our approach is capable of effectively promoting the performance of intelligent healthcare.

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review.

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing.