Applications In Healthcare Systems Research Articles

ZrO2 nanoparticle embedded reusable and self-standing biopolymeric membrane for efficient piezodynamic eradication of gram-positive and gram-negative coliform bacteria

Bacterial infections pose a significant global threat, especially with the increasing prevalence of antibiotic resistance and multidrug resistance (MDR). To address this challenge, dynamic antibacterial therapies are becoming increasingly important, with piezodynamic therapy emerging as a particularly promising approach due to its non-invasive nature and quick response using mechanical stimuli to target bacterial contaminations. This study, probably for the first-time, introduced an advanced piezo-active membrane, ZrO2 nanoparticle-loaded chitosan biopolymer (ZO@CHS), which utilized piezodynamic therapy to combat both Gram-positive (E. faecalis) and Gram-negative (E. coli) coliform bacteria. By combining the biocompatibility of chitosan with the enhanced physiochemical properties of ZrO2, this self-supporting and non-toxic highly polarized bio-nanocomposite (ZO@CHS) membrane generated a significant piezo-voltage of 4.47 V upon finger tapping and promoted cellular adhesion while enhancing reactive radicals’ production under mild ultrasound (∼15 kHz), eliminating over 96 % of E. coli and 97 % E. faecalis within just 20 minutes, which corroborated well with the statistical analysis. Detailed study, including reactive oxygen species (ROS) assessment through Photoluminescence spectroscopy and bacterial FESEM examination, provided insights into the underlying mechanism. Additionally, the membrane’s robust structure and hydrophobic properties allow for cyclic reuse, retaining over 95 % effectiveness after five cycles. Furthermore, the superior biocompatibility of the membrane, evidenced by a minimal haemolysis rate of 0.11 %, making it suitable for potential in vivo applications. Thus, this novel membrane presented promising applications in healthcare systems and pigment industries, serving as a self-cleaning material.

Body-attachable multifunctional electronic skins for bio-signal monitoring and therapeutic applications

The lack of infrastructure and accessibility in medical treatments has been considered as a global chronic issue since the concept of treatment and prevention was presented. After the COVID-19 pandemic, the medical reaction capability for epidemic outbreak/spread has been spotlighted as a critical issue to the fore worldwide. To reduce the burden on the medical system from the simultaneous disease emergence, the personalized wearable electronic systems have arisen as the next-generation biomedical monitoring/treating equipment for infectious diseases at the initial stage. In particular, electronic skin (e-skin) with its potential for multifunctional extendibility has been enabled to be applied to next-generation long-term healthcare devices with real-time biosignal sensing. Here, we introduce the recent enhancements of various e-skin systems for healthcare applications in terms of material types and device structures, including sensor components, biological signal sensing mechanisms, applicable technological advancements, and medical utilization.

Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan.

The advent of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. Consent was taken from all participants before initiating the questionnaire. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AI's impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n = 610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 ± 12.5 years. Most of the participants (78.7%, n = 480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n = 429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n = 405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training.

User-trust centric lightweight access control for smart IoT crowd sensing applications in healthcare systems

User-trust centric lightweight access control for smart IoT crowd sensing applications in healthcare systems

A Blockchain Based Data Sharing System for Health Care Applications

Researchers recently were battling to produce an ever-more effective prediction model. Studies have found that when the artificial intelligence (AI) model is trained with a wealth of information, it performs better and generalizes better. Research institutions, testing labs, hospitals, and other organizations can share their information and work together to improve the training algorithm. All companies wanted to respect the confidentiality of their data, but they still want effective and precise teaching methods for a spectrum of uses. Regarding the ethical and regulatory concerns about medical data privacy, data sharing among numerous organizations is limited. In health care systems,we describe a unique solution that integrates locally taught AI from over blockchain for enhancing disease prediction by addressing the identified gap, while maintaining anonymity and allowing data exchange. Using the information to ensure privacy poses a number of issues. This study provides a Modified Needleman-Wunsch algorithm (MNWA) for data sharing. The Artificial Neural Network (ANN) model is used for trust-enabled smart contracts. Python software performs the implementation work. A detailed experimental survey was performed to test the importance of our proposed strategy for effective early prediction and diagnosis. The proposed model's extensive experiments demonstrate a proposed method may detect ailments and boost efficiency.

Biometric identity recognition based on contrastive positive-unlabeled learning

With the development of neural networks, identity recognition techniques utilizing human physiological signals have found applications in healthcare systems. However, many current physiological signal datasets have to spend a lot of resources in the pre-labeling phase, which leads to longer training times. To reduce the cost of labeling, we propose a self-supervised time-series representation learning framework based on contrastive positive-unlabeled learning (TS − CPUL), to learn the representations of temporal neighborhood sequences. To obtain the representations, the unit root test divides the unlabeled time series into neighborhood samples and unlabeled samples. Secondly, we use neighborhood sample representations and unlabeled sample representations to do self-supervised contrastive learning and positive-unlabeled learning with a negative sample selector. Both approaches can deepen the model’s understanding of the sample representation. Lastly, to enhance the model’s capability in discriminating between ambiguous sample pairs, we design the distance-polarized matrix. The proposed TS-CPUL is evaluated through experiments on three physiological signal datasets. The results demonstrate its effectiveness in understanding time-series representations and performing well in classification. For the identity recognition task, we achieve accuracy comparable to the supervised model by fine-tuning the model with only 10% of the labeled data.

Bio-Electronics Interface between Electronics and Biological Systems for Healthcare Applications.

The intersection of electronics and biology has led to the emergence of bio-electronics interfaces, which hold tremendous potential for revolutionizing healthcare applications. These interfaces facilitate seamless communication between electronic devices and biological systems, enabling a range of diagnostic, therapeutic, and monitoring capabilities with unprecedented precision and efficiency.This paper provides a comprehensive overview of the recent advancements in bio-electronics interfaces and their diverse applications in healthcare. The fundamental principles underlying the integration of electronics with biological systems, including the design and fabrication of bio-compatible materials, signal transduction mechanisms, and biointegration strategies are discussed. [1] The paper discusses specific healthcare applications enabled by bio-electronics interfaces, such as bio-sensing for disease diagnosis, neural interfaces for brain-machine communication and prosthetics, bio-electronic implants for targeted drug delivery and neuromodulation, and wearable devices for continuous health monitoring. We also examine the challenges and opportunities associated with the development and implementation of bio-electronics interfaces in healthcare settings. These challenges include biocompatibility issues, signal interference, power management, data security, and regulatory considerations. However, rapid advancements in materials science, microfabrication techniques, wireless communication, and machine learning are driving innovation and overcoming these hurdles. We highlight the potential of bio-electronics interfaces to transform personalized medicine by enabling real-time monitoring of physiological parameters, early detection of diseases, and personalized therapeutic interventions tailored to individual patients' needs. By integrating electronic devices with biological systems at the molecular, cellular, and organismal levels, bio-electronics interfaces have the potential to revolutionize healthcare delivery, improve patient outcomes, and enhance quality of life.

Machine Learning in Healthcare: A Review

Machine learning a new and very sophisticated technology has been developed as a large trend in the market nowadays. Machine learning is an everywhere phenomenon which is applied in different areas. It’s an irreplaceable factor in many sectors such as business, science and security and so on. Implementation of machine learning technology in healthcare is emerging as a crucial trend. The potential presented by the machine learning technologies for the healthcare field is simply stunning. Machine learning technologies are making a huge influence in healthcare. ML, a sort of technology which attempts to enhance the velocity and precision of physicians work. Machine learning algorithms are used to detect hidden patterns in medical data and provides great prediction skills. In this research, we have to examine various methods used to create effective decision assistance for medical applications. This work contributes to minimizing the gap of generating effective decision support systems for healthcare applications. Keywords: Healthcare, Machine Learning, ubiquitous, medical applications

Biosensors and its diverse applications in healthcare systems

Abstract This paper provides a thorough analysis of recent developments in radio frequency identification (RFID)-based biosensors and their uses in the medical industry. The most recent and cutting-edge technologies used in RFID sensors, as well as their operational procedures in the medical industry, are the main focus of this article. The most effective design methodology for RFID biosensors is determined by evaluating various design methodologies. The present state of the art technologies in this domain are also analyzed and discussed. This paper aims to support researchers in the development of inexpensive and widely available RFID-based biosensors that can improve medical and healthcare outcomes. The use of RFID biosensors in contemporary medical applications has demonstrated significant promise for enhancing patient care and facilitating the early diagnosis of a number of diseases. It is anticipated that RFID biosensor technology will maintain to play a significant role in the development of the medical field as it undergoes further development.

A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.

Fuzzy Logic Systems for Healthcare Applications

Artificial Intelligence Technologies (AITs) have found application in several domains, including the area of medicine. Within this context, AITs have been leveraged for purposes such as illness diagnosis and treatment, patient monitoring, and risk evaluation. By using Artificial Intelligence Technologies (AITs), it becomes feasible to create systems that facilitate the development of intelligent models for predicting not only patients' response to therapy but also the risk of illness. Due to the intricate and uncertain nature of these domains, a multitude of scholars have developed AITs, including genetic algorithms, artificial immune systems, Artificial Neural Networks (ANN), and fuzzy logic. The integration of Fuzzy Logic Systems and ANN allows the construction of intelligent and flexible systems. ANN gain novel information by changing the connections among its distinct layers. Fuzzy logic inference frameworks provide a computational model that is grounded on fuzzy set rules, theory, and fuzzy reasoning. The amalgamation of many adaptive architectures gives rise to a "Neuro-Fuzzy" system. This research paper examines fuzzy network topologies, exploring their possible applications in the medical field. Researchers have recognized that this convergence has promise for the discovery of medical patterns.

Is telemedicine worth the effort? A study on the impact of effort cost on healthcare platform with heterogeneous preferences

Telemedicine involves remote delivery of healthcare and virtual meetings between patients and doctors. However, its implementation in a traditional hospital can incur extra costs stemming from additional staffing, training, and IT infrastructure requirements. The effort needed by a doctor to treat a patient can be different depending on the mode of treatment. A principal-agent model is developed to find the optimal policy for both the agent (service provider, such as doctors) and the principal (platform, such as a hospital). We assume there are two types of service options, and the doctor prefers the service type with the lower effort cost. If the doctor’s policy is to accept both types of service options, her revenue may increase or decrease with an increase in the arrival rate of the less preferred service option. Offering only the service option with the higher average revenue is preferred if the service model is under the full control of the hospital. More importantly, the doctor and the hospital may not choose the same optimal strategies for certain values of effort costs. We show that this incentive misalignment issue can be solved by offering extra compensation for the less preferred service type of the doctor, which in turn affects the doctor’s strategy as well as the hospital’s rewards. Our study contributes to the literature on service queues with heterogeneous demands, as well as research on implementing telemedicine applications in healthcare systems.

Design of a novel wearable system for healthcare applications: applying the user-centred design approach to SensHand device

Design of a novel wearable system for healthcare applications: applying the user-centred design approach to SensHand device

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications.

Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention. This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery. This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed. Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements. The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

FPGA Based Efficient OFDM Based Design and Implementation for Data and Image Transmission for Healthcare

Enormous growth in telecommunication industry demands for high speed data transmission with better quality of service (Qos). The telecommunication networks are offering the services which is from 1 Mbps to several Mbps of speed. However, most of the existing techniques address to assure the very high speed data for multimedia communication. The multimedia data may find a suitable application in healthcare system. The OFDM modulation technique promises to provide the multimedia services at rather high speed using the spectrum more efficient compared traditional scheme like TDMA, FDMA. The orthogonality of carriers eliminates the interference among the closely packed carriers and offers comparatively efficient bandwidth. The OFDM design requires choosing proper parameter selection. Important feature of OFDM is that the multipaths are effectively eliminated by choosing a higher cyclic prefix values which, gives significant results but causing more energy loss. This paper presents an efficient design for OFDMtransceiver by using FPGA. The design is modeled and simulated using Matlab Simulink and finally the design is coded using Verilog RTL and simulated in modelsim and synthesizing and implementation is done using in Xilinx EDA tool. The image type of data is taken for transmission in the proposed OFDM transceiver system. The received image type data achieves PSNR value of 29.920dB and the binary input data achieves 36.06% improvement in power utilization and less area overhead. The paper also shows the improvement in area and power compared to existing authors.

A blockchain-based secured system using the Internet of Medical Things (IOMT) network for e-healthcare monitoring

The Internet of Things (IoT) has expanded because of the development of information technology. IoT impacts several fields, including the medical profession, smart cities, and data management. The development of sustainable medical systems is significantly aided by the Internet of Medical Things (IoMT). IoMT has a big impact on healthcare since it makes it easier to monitor and validate data about patients before storing it in the cloud. Keeping all data confidential and secured is essential since the IoMT is a big-data platform that is expanding quickly.This paper will apply this technical advancement to the realm of health, namely e-health. The research provides an IoMT-based monitoring tool for personal health surveillance. Block chain technology may resolve present interoperability issues in health-related systems and offer technological solutions that allow the electronic transfer of medical records to medical organizations and medical specialists. However, the transmission and security of people's health data remain a major concern in IoMT devices across a wide range of product categories. This research uses block chain as a secure solution to satisfy the requirements of medical privacy and data security. In this study, a Block chain-based IoMT security System (BC-IoMT-SS) is presented based on BC technology in IoMT for the security, privacy, and management of patient information. The suggested framework is successfully implemented to fulfill the optimal privacy and safety criteria for medical data management in IoMT gadgets. Utilizing the block chain's key, a healthcare application system has been created in which the person's health data may safely generate alerts for verified healthcare practitioners. Simulation results demonstrate that the suggested BC-IoMT-SS technique produces a high precision ratio of 94%, an efficiency ratio of 94%, a shorter delay of 0.63s, and a shorter reaction time compared to other current methods.

An Extracurricular Project-Based Training Course in Innovation and Entrepreneurship Delivered to a Transdisciplinary Group of Students in Engineering, Social Sciences, Arts and Medicine

Limited information is available on the design of combined innovation and entrepreneurship training courses, and with even less available on delivering such courses to multidisciplinary teams. We designed an extracurricular project-based training course in innovation and entrepreneurship and delivered it to transdisciplinary teams of students who were then asked to build an autonomous mobile system for healthcare applications and to create a business model to implement it. The course was created through a backward educational design and design thinking to assist teams of students in transforming an idea into a technical solution and developing it into a business model through a project-based learning experience. A transdisciplinary group of 31 students, mostly in bachelor’s degree programs, worked either all together, in plenary sessions, in subject-specific groups, or in teams of 5–6 members, under the guidance of their trainers during a ten-day summer school. We used questionnaires to evaluate the changes in student perceptions and a satisfaction survey to evaluate the students’ experience. The qualitative observations together with the results of the quantitative instruments revealed positive changes in student perceptions towards innovation and entrepreneurship. Additionally, the satisfaction survey offered positive feedback and some recommendations for further improvement. The implementation of the course showed that planning is important, but flexibility is essential, that covering fewer topics might lead to deeper understanding, and that managing expectations and practicing empathy are crucial.

Nature-inspired spider web shaped UHF RFID reader antenna for IoT and healthcare applications

This paper proposes a nature-inspired spider web-shaped ultra-high frequency (UHF) radio frequency identification (RFID) reader antenna and battery-free sensor-based system for healthcare applications. This antenna design consists of eight concentric decagons of various sizes and five straight microstrip lines.These lines are connected to the ground using 50 Omega resistors from both ends, except for one microstrip line that is reserved for connecting a feeding port. The reader antenna design features fairly strong and uniform electric and magnetic field characteristics. It also exhibits wideband characteristics, covering whole UHF RFID band (860–960 MHz) and providing a tag reading volume of 200 times 200 times 20 mm{^3}. Additionally, it has low gain characteristics, which are necessary for the majority of nearfield applications to prevent the misreading of other tags. Moreover, the current distribution in this design is symmetric throughout the structure, effectively resolving orientation sensitivity issues commonly encountered in low-cost linearly polarized tag antennas. The measurement results show that the reader antenna can read medicine pills tagged using low-cost passive/battery-free RFID tags, tagged expensive jewelry, intervenes solution, and blood bags positioned in various orientations. As a result, the proposed reader antenna-based system is a strong contender for near-field RFID, healthcare, and IoT applications.

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification.

Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.

The Advance of Multi-Round Dialogue System with Deep Learning

Multi-turn dialog systems have seen significant advances in recent years, driven by various approaches. The Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method have all shown promising results in advancing the state-of-the-art in this field. However, challenges remain in developing models that can handle complex and diverse user inputs and generate responses that are not only informative but also engaging and natural. Further research is needed to address these challenges and advance state of art in multi-turn dialogue systems. This paper reviews four critical methods for improving the quality of multi-turn dialogue systems: Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method. The Multi-layer Semantic Method utilizes multi-layer neural networks to model dialogue context and generate responses with improved coherence and relevance. The reinforcement Learning Method employs a reward-based approach to optimize response generation by training models to maximize long-term dialogue success. The knowledge Graph Method incorporates external knowledge sources, such as knowledge graphs, to enrich the dialogue context and improve response quality. The Medicine Knowledge Graph Method focuses on integrating medical knowledge into dialogue systems for healthcare applications. Each of these methods has demonstrated promising results in enhancing the quality of multi-turn dialogue systems.