Proposal for Medical Data Transmission in Healthcare Systems

Background: Information systems used in hospitals are slow and consume a lot of system memory, facilitating crashes, impacting patients seeking consultation face long waiting periods by a medical specialist; Still considering that exchange patient data and medical consultations in system interconnected between hospitals, for scheduling of consultations may become even more latent.Methods: Aiming to solve such problems, the present study implements modeling with discrete-event technology applied to a healthcare system, modulating the signal transmitted with the DQPSK format, through the simulation environment, the Simulink of the MATLAB software, improving the transmission of data, through a pre-coding process of bits adopting discrete events in the signal before modulation.Results: This study aims to increase the information capacity for healthcare systems, bringing a new approach for signal transmission, undertaken in the discrete domain employing the discrete entities in the bit generation process, this use being the differential applied on the bit itself, in the physical layer, showing better computational performance regarding memory utilization related to compression of information, showing an improvement of 101.52%.Conclusion: The proposal developed has the properties of improving the capacity of hospital services and can increase the performance of the communication between all medical devices, this positive impact is the result that the data stream will consume fewer communication resources

A Bayesian Analysis of Spectral ARMA Model

Bezerra et al. (2008) proposed a new method, based on Yule-Walker equations, to estimate the ARMA spectral model. In this paper, a Bayesian approach is developed for this model by using the noninformative prior proposed by Jeffreys (1967). The Bayesian computations, simulation via Markov Monte Carlo (MCMC) is carried out and characteristics of marginal posterior distributions such as Bayes estimator and confidence interval for the parameters of the ARMA model are derived. Both methods are also compared with the traditional least squares and maximum likelihood approaches and a numerical illustration with two examples of the ARMA model is presented to evaluate the performance of the procedures

Hematology and Digital Image Processing: Watershed Transform-Based Methodology for Blood Cell Counting Using the WT-MO Algorithm

Background: Most diseases can be detected by routine examination, even if they are in the initial phase. Currently, one of the most requested medical laboratory tests is that which allows detecting from bacterial infections until leukemias. However, for less favored populations, this examination can be seen as having a high cost. Methods: Thus, this study introduces an algorithm of segmentation of images capable of detecting and counting red blood cells and leukocytes present in digital images of blood smear. The methodology was named by WT-MO, once it relies on the concepts of Watershed Transform and Morphological Operations. The experiments were conducted in the MATLAB software simulation environment, where 25 images were used in order to evaluate the accuracy, processing time, and execution time of the WT-MO algorithm. Results: The results show that the WT-MO methodology presents high accuracy, reaching 96% and 92% in the red blood cell and leukocyte counts, respectively; reliability and low processing time, reaching an average processing time and execution time, achieving from 0.74 to 2.17 seconds. Therefore, the WT-MO algorithm can be seen as the first step in making laboratory tests more accessible to populations in underdeveloped and developing countries. Conclusion: The WT-MO methodology helps not only disadvantaged populations gain access to low-cost, high-reliability tests but also has excellent potential for use in laboratories in developed countries

The Axis “Human Papillomavirus - Anal Squamous Cell Carcinoma”: A Review

Background: Anal Squamous Cell Carcinoma (ASCC) is an infrequent neoplasia that represents 2% of the digestive tumors and it has a growing incidence. Objective: This investigation (i) studies the pathogenesis of an increasingly prevalent disease, (ii) its treatment and prognosis along with (iii) a bibliographical review of the main characteristics of the Human Papillomavirus (HPV) as well as its effects on humans. Methods: A literature review is performed, comprising articles up to 2019 and cross-research manuscripts with the initial research. Results: Several studies demonstrate the HPV role as a significant risk factor to the development of ASCC, as well as its higher incidence in HIV-positive individuals and in those who engage in receptive anal intercourse. Future trends in theragnostic using information technology are examined. Conclusions: ASCC is a neoplasm mostly associated with HPV. Many studies are needed to improve the treatment as well as in the evaluation of the tumor characteristics

Health 4.0: Applications, Management, Technologies and Review

The Industry 4.0 Standard (I4S) employs technologies for automation and data exchange through cloud computing, Big Data (BD), Internet of Things (IoT), forms of wireless Internet, 5G technologies, cryptography, the use of semantic database (DB) design, Augmented Reality (AR) and Content-Based Image Retrieval (CBIR). Its healthcare extension is the so-called Health 4.0. This study informs about Health 4.0 and its potential to extend, virtualize and enable new healthcare-related processes (e.g., home care, finitude medicine, and personalized/remotely triggered pharmaceutical treatments) and transform them into services. In the future, these services will be able to virtualize multiple levels of care, connect devices and move to Personalized Medicine (PM). The Health 4.0 Cyber-Physical System (HCPS) contains several types of computers, communications, storage, interfaces, biosensors, and bioactuators. The HCPS paradigm permits observing processes from the real world, as well as monitoring patients before, during and after surgical procedures using biosensors. Besides, HCPSs contain bioactuators that accomplish the intended interventions along with other novel strategies to deploy PM. A biosensor detects some critical outer and inner patient conditions and sends these signals to a Decision-Making Unit (DMU). Mobile devices and wearables are present examples of gadgets containing biosensors. Once the DMU receives signals, they can be compared to the patient’s medical history and, depending on the protocols, a set of measures to handle a given situation will follow. The part responsible for the implementation of the automated mitigation actions are the bioactuators, which can vary from a buzzer to the remote-controlled release of some elements in a capsule inside the patient’s body.             Decentralizing health services is a challenge for the creation of health-related applications. Together, CBIR systems can enable access to information from multimedia and multimodality images, which can aid in patient diagnosis and medical decision-making. Currently, the National Health Service addresses the application of communication tools to patients and medical teams to intensify the transfer of treatments from the hospital to the home, without disruption in outpatient services. HCPS technologies share tools with remote servers, allowing data embedding and BD analysis and permit easy integration of healthcare professionals expertise with intelligent devices.  However, it is undeniable the need for improvements, multidisciplinary discussions, strong laws/protocols, inventories about the impact of novel techniques on patients/caregivers as well as rigorous tests of accuracy until reaching the level of automating any medical care technological initiative

Why Software-Defined Radio (SDR) Matters in Healthcare?

Background: Wireless Body Area Networks (WBANs) have been drawing noteworthy academic and industrial attention. A WBAN states a network dedicated to acquire personal biomedical data via cutting-edge sensors and to transmit healthcare-related commands to particular types of actuators intended for health purposes. Still, different proprietary designs exist, which may lead to biased assessments. This paper studies the role of Software-Defined Radio (SDR) in a WBAN system for inpatient and outpatient monitoring and explains to health professionals the importance of the SDR within WBANs. Methods: A concern related to all wireless networks is their dependence on hardware, which limits reprogramming or reconfiguration alternatives. If an error happens in the equipment, firmware, or software, then, typically, there will be no way to fix system vulnerabilities. SDR solves many fixed-hardware problems with other benefits. Results: SDR entails more healthcare domain dynamics with more network convergence in agreement with the stakeholders involved. Then the SDR perspective can bring in innovation to the healthcare subsystems’ interoperability with recombination/reprogramming of their parts, updating, and malleability. Conclusion: SDR technology has many utilizations in radio environments and is becoming progressively more widespread among all kinds of users. Nowadays, there are many frameworks to manipulate radio signals only with a computer and an inexpensive SDR arrangement. Moreover, providing a very cheap radio receiver/transmitter equipment, SDR devices can be merged with free software to simplify the spectrum analyses, provide interferences detection, deliver efficient frequency distribution assignments, test repeaters' operation while measuring their parameters, identify spectrum intruders and characterize noise according to frequency bands

Content-Based Image Retrieval (CBIR) in Big Histological Image Databases

Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge. Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management. Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests. Conclusion: This research effort significant contributions are twofold. 1) Defining a  comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods