Applications In Healthcare Systems Research Articles

Bacteriophage Proteome: Insights and Potentials of an Alternate to Antibiotics.

IntroductionThe mounting incidence of multidrug-resistant bacterial strains and the dearth of novel antibiotics demand alternate therapies to manage the infections caused by resistant superbugs. Bacteriophages and phage=derived proteins are considered as potential alternates to treat such infections, and have several applications in health care systems. The aim of this review is to explore the hidden potential of bacteriophage proteins which may be a practical alternative approach to manage the threat of antibiotic resistance.ResultsClinical trials are in progress for the use of phage therapy as a tool for routine medical use; however, the existing regulations may hamper their development of routine antimicrobial agents. The advancement of molecular techniques and the advent of sequencing have opened new potentials for the design of engineered bacteriophages as well as recombinant bacteriophage proteins. The phage enzymes and proteins encoded by the lysis cassette genes, especially endolysins, holins, and spanins, have shown plausible potentials as therapeutic candidates.ConclusionThis review offers an integrated viewpoint that aims to decipher the insights and abilities of bacteriophages and their derived proteins as potential alternatives to antibiotics.

Design of Reliable, Secure and Intelligent Systems for Healthcare Applications

Design of Reliable, Secure and Intelligent Systems for Healthcare Applications

Deep learning applications for IoT in health care: A systematic review

In machine learning, deep learning is the most popular topic having a wide range of applications such as computer vision, natural language processing, speech recognition, visual object detection, disease prediction, drug discovery, bioinformatics, biomedicine, etc. Of these applications, health care and medical science-related applications are dramatically on the rise. The tremendous big data growth, the Internet of Things (IoT), connected devices, and high-performance computers utilizing GPUs and TPUs are the main reasons why deep learning is so popular. Based on their specific tasks, medical IoT, digital images, electronic health record (EHR) data, genomic data, and central medical databases are the primary data sources for deep learning systems. Several potential issues such as privacy, QoS optimization, and deployment indicate the pivotal part of deep learning. In this paper, deep learning for IoT applications in health care systems is reviewed based on the Systematic Literature Review (SLR). This paper investigates the related researches, selected from among 44 published research papers, conducted within a period of ten years – 2010 to 2020. Firstly, theoretical concepts and ideas of deep learning and technical taxonomy are proposed. Afterwards, major deep learning applications for IoT in health care and medical sciences are presented through analyzing the related works. Later, the main idea, advantages, disadvantages, and limitations of each study are discussed, preceding suggestions for further research.

On Mathematical Analysis of Active Drug Transport Coupled With Flow-Induced Diffusion in Blood Vessels.

Blood vessels are flow-induced diffusive molecular channels equipped with transport mechanisms across their walls for conveying substances between the organs in the body. Mathematical modeling of the blood vessel as a molecular transport channel can be used for the characterization of the underlying processes and higher-level functions in the circulatory system. Besides, the mathematical model can be utilized for designing and realizing nano-scale molecular communication systems for healthcare applications including drug delivery systems. In this paper, a continuous-time Markov chain framework is proposed to simply model active transport mechanisms e.g. transcytosis, across the single-layered endothelial cells building the inner vessel wall. Correspondingly, a general homogeneous boundary condition over the vessel wall is introduced. Coupled with the derived boundary condition, the flow-induced diffusion problem in an ideal vessel structure with a cylindrical shape is accurately formulated which takes into account variation in all three dimensions. The corresponding concentration Green's function is analytically derived in terms of a convergent infinite series. Particle-based simulation results confirm the proposed analysis. Also, the effects of system parameters on the concentration Green's function are examined.

Face and Gesture Analysis for Health Informatics.

The goal of Face and Gesture Analysis for Health Informatics's workshop is to share and discuss the achievements as well as the challenges in using computer vision and machine learning for automatic human behavior analysis and modeling for clinical research and healthcare applications. The workshop aims to promote current research and support growth of multidisciplinary collaborations to advance this groundbreaking research. The meeting gathers scientists working in related areas of computer vision and machine learning, multi-modal signal processing and fusion, human centered computing, behavioral sensing, assistive technologies, and medical tutoring systems for healthcare applications and medicine.

RETRACTED ARTICLE: Multilevel Tetrolet transform based breast cancer classifier and diagnosis system for healthcare applications

Breast cancer is a deadly disease and nowadays affects middle-aged women. Doctors use various methods to find out the presence of micro calcifications. In general, images of digital mammogram are used for cancer diagnosis. But the precision that determines the presence of cancer requires multiple restrictions. An improved medical decision support system for the classification of mammogram images using Unsupervised Test Vector Optimization (UTVO) based classifier is developed for health care and is presented in this work. The median filtering in the preprocessing stage removes the speckle, salt and pepper noises for mammogram image enhancement and the multilevel Tetrolet transformation based Matrix vectors are obtained for feature extraction. Multilevel Tetrolet transformation based features (MTF) are used for the selection of texture features. The UTVO classification approach proposed for identifying breast cancer exploits the potential and analyzes its performance after classification of benign and malignant cases. Finally, the UTVO classification system’s memory and computational time has been used to weigh the optimal weight and used to analyze the performance without compromise. The proposed system is expected to provide the best support for doctors to achieve maximum accuracy and improvement in health care.

A Review on Therapeutic Potentials of Crocetin-A Carotenoid Derived from Saffron

Natural dyes recognized as carotenoids have found application in health care system due to their wide biological activities, high safety margins and lower cost. Crocetin, a carotenoid constituent of saffron has shown wide range of pharmacological applications due to its significant antioxidant properties. It is used as antifungal, antimicrobial and antiallergic agent. Crocetin has shown significant potential as an anti- tumor agent in animal models and cell culture systems. Current review has shown crocetin’s multispectrum pharmacological benefits for the treatment of various chronic diseases such as cancer, diabetes, parkinson’s and alzheimer’s disease. This review focus on various studies done on crocetin explaining its broa d spectrum pharmacological activities. In addition, this review will also focus on the pharmacokinetic studies of crocetin on human and animals. Crocetin is a highly potent molecule because of its strong antioxidant properties. In the near future, increasing its bioavailability using novel drug delivery methods having minimum side effects will get this promising natural molecule to the forefront of therapy for the treatment of various chronic human diseases like cancer, diabetes, parkinson’s and alzheimer’s disease.

Reviews of wearable healthcare systems: Materials, devices and system integration

Wearable health monitoring systems are considered as the next generation of personal portable devices for telemedicine practice. These systems are based on monitoring different kinds of biological signals released by human beings through saliva, urine, breathing and epidemic skin perspiration. However, the development and commercialization of wearable healthcare equipment is still at a relatively slow pace, because the construction method of ordinary semiconductor equipment is no longer applicable. Despite these challenges, advances in materials science, chemical analysis techniques, equipment design and manufacturing methods have laid the foundation for a completely different wearable technology, which has led to the continuous evolution of wearable systems over the years. Hence, a comprehensive review in this field is required for the rational design and development of wearable systems for healthcare applications. In this regard, we like to summarize all the major components in wearable healthcare systems and discuss the progress made in this field, as well as the challenges that lie ahead. We think that this review will have a great implication for the wider scientific community which will contribute to the rational design of human health monitoring devices and accelerate the development of flexible electronics.

A Personalized Beat-to-Beat Heart Rate Detection System From Ballistocardiogram for Smart Home Applications

This paper presents a ballistocardiogram (BCG) based beat-to-beat heart rate (HR) measurement system for healthcare applications in smart home. The BCG signal is picked up by two parallel connected piezoelectric elements, and processed by a robust HR detection algorithm. To deal with the diversity in BCG signal, a personalized heartbeat template is extracted to handle the diversity in BCG signal. The Pearson correlation coefficient is used to correlate between personalized template and the BCG signal. Heartbeats are detected by using the Hilbert Transform. The system is verified on 32 subjects with simultaneously recorded BCG and ECG signals. With the reference to ECG signal, we have achieved mean absolute relative error of the beat-to-beat intervals of 4.56%, and average absolute error ±standard deviation of absolute error for HR estimation in a 30-seconds window of 0.59 ±0.56 beats per minute. Meanwhile, 99.67% of the beat-to-beat intervals are detected on average.

An Overview of Information and Communication Technology Tools Application in Healthcare System

Information and communication technology (ICT) is improving the health care services throughout the world. In health care system, ICT involve application of information and communication processing using computer for storage, recovery, sharing and use of knowledge on uses of medicines and patient-centred services. Similarly, web-based system, e-prescription, automated dispensing system, mobile technology and innovations in ICT tools are giving a way to access the data in health care sector all over the world. In healthcare system, ICTs can enhance the qualities of services to dispense the medicines and to maintain the data on patients medication history and patients safety. With this information, this review article mainly focused on overview of ICT tools application in healthcare system.   How to cite this article:Dineshkumar B, Krishnakumar K, Nair SK, Paul D. An Overview of Information and Communication Technology Tools Application in Healthcare System. J Adv Res Med Sci Tech 2019; 6(1&2): 15-17.

Parameterizing neural networks for disease classification

AbstractNeural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set‐up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency.

An efficient approach for the detection of link failures in WBAN system for health care applications

SummaryWireless body area network (WBAN) plays an important role in patient health care. The performance of this WBAN system is affected by link failures due to the presence of malicious sensor nodes. Hence, the detection and mitigation of this link failure is important for improving the efficiency of the WBAN system. This paper proposes a methodology for link failure detection using weight metric approach. The performance of the proposed methodology is analyzed in terms of packet delivery ratio (PDR), link failure detection latency, and link failure detection rate.

Fundamentals, Recent Advances, and Future Challenges in Bioimpedance Devices for Healthcare Applications

This work develops a thorough review of bioimpedance systems for healthcare applications. The basis and fundamentals of bioimpedance measurements are described covering issues ranging from the hardware diagrams to the configurations and designs of the electrodes and from the mathematical models that describe the frequency behavior of the bioimpedance to the sources of noise and artifacts. Bioimpedance applications such as body composition assessment, impedance cardiography (ICG), transthoracic impedance pneumography, electrical impedance tomography (EIT), and skin conductance are described and analyzed. A breakdown of recent advances and future challenges of bioimpedance is also performed, addressing topics such as transducers for biosensors and Lab-on-Chip technology, measurements in implantable systems, characterization of new parameters and substances, and novel bioimpedance applications.

RFID Applications in Healthcare Systems From an Operational Perspective

This treatise centers on media management and entrepreneurship as the healthcare industry transforms legacy systems into lean strategies that leverage RFID-related technologies with solutions to lower cost for supply chain partners and simultaneously enhance patient value. These solutions include using barcodes as a backup to more advanced media management technologies, standardization in medical processes and automation, overcoming implementation and resistance considerations, and eliminating waste in hospital inventory management systems. Successful case studies are outlined to illustrate these principles in practice.

Design and Implementation of High Power LED Lighting System for Health Care Applications

Background:A conventional front-end rectifier causes line current distortion and reduces the power factor, which result in lowering power quality for Light Emitting Diode (LED) drive system. Hence, this paper projects the design, simulation and comparison of novel PI tuned by the fuzzy logic controller with the conventional PI controller for modified SEPIC rectifier to produce the required load voltage along with supply-side unity power factor and less distorted supply current with limited harmonic content for LED lighting in healthcare applications. A prototype of 100W, 48V LED driver was developed for testing the performance of the controller.Methods:This paper presents the modified SEPIC LED driver with PI integrated fuzzy and classical PI for controlling voltage. For controlling source current, classical PI is chosen. Both are equipped with the modified SEPIC rectifier. Both conventional PI control and novel fuzzy tuned performances were compared.Results:The proposed control topology operated modified SEPIC rectifier was analyzed and the corresponding power factor and THD were measured. The operational evaluation of the proposed LED driver using fuzzy tuned-PI/PI controller combinations for different output power is provided in Table 2. Sustained regulated DC voltage of 48V was achieved even when the load resistance varied within a specific range. Power factor of 0.9995, which is close to unity, was also achieved. The relative analysis was made with conventional PI and trendy PI integrated FLC controller which is provided in Table 3. The usage of PI integrated fuzzy logic controller minimized the peak overshoot to be around 1.3% and rise time of 0.5s which are lower when compared to the conventional PI controller. With reference to Fig. (8a), the source current THD of the conventional PI controller was observed to be around 7.39%. Having PI integrated FLC, THD was further reduced and for rated load, it was found to be 1.39%. The power factor of the conventional PI controller is around 0.9974. PI integrated fuzzy logic controller improved the power factor to 0.9995 with fuzzy tuned PI controller in action as shown in Fig. (8b). A prototype of 100W, 48V LED driver was developed for testing the performance of the controller. A power quality analyser was employed for measuring power factor and THD, shown in Fig. (10a). 3.633% of harmonic distortions at the source current and 0.9980% of input power factor was achieved for rated load power. 4.510% of supply current THD with 0.9931% input power factor was achieved for low load power.Conclusion:This manuscript suggests a modified single switch SEPIC LED driver for 48V output operated healthcare equipment. Simulation study of this driver shows the better performance. In order to analyze the performance, a comparative study was conducted by using the classical PI and the novel PI integrated fuzzy controller. Satisfactory results regarding enhanced quality of power, regulated load voltage, quick rise time and settling time were achieved. The source current THD has been reduced to around 1.39% which is less than 5% as per the IEEE-516 prescribed standard and the power factor has been improved to 0.9995 by implementing the fuzzy tuned PI controller. The above results favor the suggested modified SEPIC LED driver for practical healthcare applications.

Data Quality Methods and Applications in Health Care System: A Systematic Literature Review

The use of data cataloging tools allows keeping different records of both qualitative and quantitative information. However, the large amount of data is not always synonymous with quality, in the medical field this argument becomes even more critical if it is considered the consequences that the lack of a systematic and rigorous process can have for patients. The analysis was conducted through a systematic review includes several general cases and practical methodologies of data quality analysis in the health context. The search for results was made using the keywords "data quality" and "health." The study considers publications made from 2014 to 2018, topic related to Business, Management and Accounting, exclusively in the case of the Tutto platform peer-review journals were chosen, English language of publication. Efficient use of information requires databases that can collect and order health information. However, this is the first step, data quality attempts to go further through the creation of qualitative or statistical control processes and indicators able to ascertain the lack of data or identify potential anomalies. The conducted analysis sets the stage for future quality implementation in the clinical pathway and patient management.

SynSys: A Synthetic Data Generation System for Healthcare Applications.

Creation of realistic synthetic behavior-based sensor data is an important aspect of testing machine learning techniques for healthcare applications. Many of the existing approaches for generating synthetic data are often limited in terms of complexity and realism. We introduce SynSys, a machine learning-based synthetic data generation method, to improve upon these limitations. We use this method to generate synthetic time series data that is composed of nested sequences using hidden Markov models and regression models which are initially trained on real datasets. We test our synthetic data generation technique on a real annotated smart home dataset. We use time series distance measures as a baseline to determine how realistic the generated data is compared to real data and demonstrate that SynSys produces more realistic data in terms of distance compared to random data generation, data from another home, and data from another time period. Finally, we apply our synthetic data generation technique to the problem of generating data when only a small amount of ground truth data is available. Using semi-supervised learning we demonstrate that SynSys is able to improve activity recognition accuracy compared to using the small amount of real data alone.

Electronic and Thermal Properties of Graphene and Recent Advances in Graphene Based Electronics Applications.

Recently, graphene has been extensively researched in fundamental science and engineering fields and has been developed for various electronic applications in emerging technologies owing to its outstanding material properties, including superior electronic, thermal, optical and mechanical properties. Thus, graphene has enabled substantial progress in the development of the current electronic systems. Here, we introduce the most important electronic and thermal properties of graphene, including its high conductivity, quantum Hall effect, Dirac fermions, high Seebeck coefficient and thermoelectric effects. We also present up-to-date graphene-based applications: optical devices, electronic and thermal sensors, and energy management systems. These applications pave the way for advanced biomedical engineering, reliable human therapy, and environmental protection. In this review, we show that the development of graphene suggests substantial improvements in current electronic technologies and applications in healthcare systems.

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.

Self-Powered Wearable Pressure Sensors with Enhanced Piezoelectric Properties of Aligned P(VDF-TrFE)/MWCNT Composites for Monitoring Human Physiological and Muscle Motion Signs.

Self-powered operation, flexibility, excellent mechanical properties, and ultra-high sensitivity are highly desired properties for pressure sensors in human health monitoring and anthropomorphic robotic systems. Piezoelectric pressure sensors, with enhanced electromechanical performance to effectively distinguish multiple mechanical stimuli (including pressing, stretching, bending, and twisting), have attracted interest to precisely acquire the weak signals of the human body. In this work, we prepared a poly(vinylidene fluoride-trifluoroethylene)/ multi-walled carbon nanotube (P(VDF-TrFE)/MWCNT) composite by an electrospinning process and stretched it to achieve alignment of the polymer chains. The composite membrane demonstrated excellent piezoelectricy, favorable mechanical strength, and high sensitivity. The piezoelectric coefficient d33 value was approximately 50 pm/V, the Young’s modulus was ~0.986 GPa, and the sensitivity was ~540 mV/N. The resulting composite membrane was employed as a piezoelectric pressure sensor to monitor small physiological signals including pulse, breath, and small motions of muscle and joints such as swallowing, chewing, and finger and wrist movements. Moderate doping with carbon nanotubes had a positive impact on the formation of the β phase of the piezoelectric device, and the piezoelectric pressure sensor has the potential for application in health care systems and smart wearable devices.