Automated Systems In Healthcare Research Articles

An Optimized Genetic Neural Network for Brain Tumour Detection

Brain tumour classification is an important function of medical research, contributing to timely and accurate treatment decisions. This research uses a convolutional neural network (CNN) optimized by genetic algorithms to classify MRI scanned images of brain tumors into four groups. The CNN algorithm uses hierarchical feature extraction layers, capturing complex tumor-specific patterns greater than traditional methods. By programmatically adjusting genetic algorithm learning rates and dropout rates, the models are normalized and enhanced in real-time to ensure efficient training, reduction of redundancy for the model work well, ensure high levels of input, dealing with changes in brightness and contrast. A grayscale MRI scans dataset was used to train and validate the system, demonstrating the ability to generalize to unseen data obtaining significant classification accuracy Visualization methods including confusion matrices and class-wise accuracy analysis for insights a delve into the predictive capabilities of the genetic algorithm Integration Therapy - highlights a new approach to improve the performance of CNNs in image analysis. The results highspot the capability of such machine learning methods to improve brain tumor diagnostic tools, providing a foundation for future developments in automated healthcare systems.

Autonomous Healthcare Systems: Deep Learning-Based IoT Solutions for Continuous Monitoring and Adaptive Treatment

Autonomous healthcare systems are a big change in the way medicine is done. They use deep learning algorithms and Internet of Things (IoT) devices to keep an eye on patients all the time and change their treatment as needed. This new way of doing things could change the way patients are cared for by giving real-time information and personalized treatments. With the help of deep learning, these systems can look at huge amounts of data produced by IoT sensors, like those in medical implants and smart tech, to spot small changes in health and spot problems before they get worse.Autonomous healthcare systems are based on their ability to constantly gather and analyze data from a variety of sources, such as vital signs, biological markers, and patient-reported complaints. Deep learning algorithms are very important to this process because they can find complicated patterns and connections in the data. These algorithms can get useful information from raw sensor data by using methods like CNN, MobileNet, and InceptionV3. This algorithm lets healthcare professionals move quickly and proactively.Also, independent healthcare systems are made to change based on the wants and interests of each patient by using personalized treatment plans. These systems can improve results and patient happiness by constantly checking how patients respond to actions and making changes to treatment plans on the fly. Adding IoT devices also makes it easier for patients and healthcare workers to talk to each other, allowing for distant discussions and quick solutions. The automated healthcare systems are a revolutionary way to provide medical care. They use deep learning-based IoT solutions to keep an eye on patients all the time and adjust their treatment as needed. These systems might be able to improve patient results, lower healthcare costs, and raise general quality of life by using the power of data-driven insights and individual actions

Examining Ghana's National Health Insurance Act, 2003 (Act 650) to Improve Accessibility of Artificial Intelligence Therapies and Address Compensation Issues in Cases of Medical Negligence

Objective: Examine Ghana’s National Health Insurance Act (Act 650) to identify coverage gaps limiting artificial intelligence (AI) therapy access and address medical negligence liability issues surrounding automated healthcare systems. 
 Methods: Legal and regulatory analysis of Act 650 were conducted, review of academic literature on global uptake of AI interventions and medical negligence principles were elucidated, examination of case studies implementing pilot AI therapy programs under insurance schemes were considered.
 Results & Conclusions: Act 650 lacks clear provisions for funding innovative AI treatments with proven efficacy and undefined negligence determination guidelines involving AI systems, contributing to accessibility and accountability issues. Proposed amendments to reimburse certain AI therapies through the National Health Insurance Scheme, expand certified provider eligibility, and institute transparent negligence compensation formulas.
 Recommendations: Reform Act 650 to support increased appropriate use of AI healthcare services, protect patients undergoing automated diagnosis/treatment, and clarify liability rules for medical negligence incidents relating to AI.
 Novelty & Significance: First extensive analysis focused on opportunities for Ghana’s health insurance framework to catalyze equitable diffusion of advanced AI therapeutics and address emerging legal challenges and safety risks as automated medicine advances.

Optimized deep belief network and unsupervised deep learning methods for disease prediction

Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient feature extraction and classification model for healthcare systems. First, several medical data related to the patient’s health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%.

Accident Attention System for Somnambulism Patients

Promising technologies such as sensors, networking, and edge have led to many smart healthcare solutions to monitor and track patient health status. The health sector is now experiencing a significant transformation from conventional patient care to a smart healthcare environment. Smart health care allows medical professionals to monitor patients remotely and visualize the disease prognosis effectively. The Internet of medical things connect patients, doctors, and medical equipment via wireless networking technologies to process the data with Artificial Intelligence models. One of the domains of automated health care systems is to alert the caregivers and hospital on emergency conditions. This research study is a novel work that aims to help the caregivers of somnambulism patients attend to them in case of emergency. Sleep quality improves the health and work efficiency of any person. The caregivers of sleepwalking patients suffer from lack of sleep as the patient gets active during the night hours. The model is based on fall detection and sleep detection from wearable sensor data. The fall detection model includes feature selection by LASSO and classification by ensemble classifier. The proposed methodology shows improved performance for the fall detection model for all ensemble machine learning classifiers.

Applicability of Machine Learning towards development of Patient Centered Decision Support System to Palliative Care Settings

Developing systems for assisting in the care of palliative patients requires an understanding of providing relief from distressing symptoms to ease suffering and improve the Quality of Life. To assess the applicability of Artificial Intelligence techniquesto Palliative care settings, it is important to understand methods suitable for symptom care and management such that they provide relief from distress due to the life-limiting illness.With rapid advancements in Artificial Intelligence, developing automated healthcare systems for patient care has revolutionized personalized care which is of significance in palliative care settings. This article summarizes the existing literature which suggests the need for improved end-of-life care for a better Quality of Life and the implementation scenarios where AI is integrated in healthcare systems to assist the palliative care team in decision making and thus enhance patient care.

Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic.

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two ways—firstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity

ObjectiveHospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process. MethodAs a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured. ResultsAs a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model. ConclusionFinally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.

IoT Based Healthcare System Model for quarantine centers Post Covid 19 Outbreak

The bright capability of showing up Internet of things (IoT) advancements for wellbeing gadgets and sensors assumed an essential job in the new age of Healthcare industry for secure patient care and maintaining social distancing. After Corona virus outbreak, it has been observed that there is an utter need for the automated healthcare system for the patients in need or elderly incapable or un-helped people. This paper proposed a Healthcare and quarantined install with IoT devices for the benefiting of quarantined people and Covid-19 patients.

Automated Health Monitoring System Using Advanced Technology

Coma or unconsciousness is a state wherein the patient cannot respond to any internal or external stimulus. In this situation, the patient has no physical control over his entire body. Such cases require a serious attention and continuous monitoring to save patient's life. Currently, monitoring coma patients critically is very expensive and needs more manpower. Besides, such continuous intensive care by a paramedical assistant are error-prone, which may lead to further complications. Thus, the need for automated healthcare systems still exist. These automated systems help in continuously monitoring and recording all the vital information of a particular subject by maintaining all the comatose records. In this article, a health monitoring system for the coma patient based on the global system for mobile (GSM) and the Internet of Things (IoT) is proposed. IoT as a new technology which facilitates the process of extracting, analyzing and sending data with high efficiency. In this proposed system, four health parameters, temperature, heartbeat, accelerometer and eye blinks are monitored. By integrating these four parameters with live monitoring module and/or a GSM module, the need for clinical staff and accompanying persons will be less as the systems allows relatives and staff to monitor the coma patient online via mobile phones or receive notification based on the patient's status changes. The results achieved by the system shows real time reading of body temperature and the heartbeat. Finally, the results obtained by the MPU-6050 gyroscope and the eye blink sensor were very satisfactory.

Energy efficient key agreement scheme for ubiquitous and continuous remote healthcare systems using data mining technique

Wireless body area networks (WBANs) based ubiquitous and fully automated healthcare systems provide a platform to share medical information. Energy efficiency and communication security will increase the confidence of the users in adopting such remote healthcare systems. Key agreement and authentication schemes play an important role in the security of remote healthcare systems. The nodes in a WBAN exchange information in order to complete the key agreement and authentication process. In the literature, numerous schemes have used heavy mathematical calculations or overloaded with excessive information exchange. This paper presents a bloom filter-based key agreement scheme using k-mean clustering for WBANs. The key agreement and authentication is performed in clustered environment using k-mean clustering. This makes the scheme more robust and energy efficient. The keys are generated from the EKG values of the human body. The proposed mechanism is energy efficient and secure, due to its more efficient key generation and less memory utilizations for remote healthcare systems. Moreover, the proposed scheme is analyzed and compared with a state-of-the-art scheme in terms of energy consumption, memory utilizations, processing complexity, and false positive rate (FPR). The results show that the proposed scheme outperform significantly the other scheme by consuming less energy and efficient memory utilization, while achieving a very low FPR and linear running complexity.

A review of the application of automation technologies in healthcare domain

Health care is a rapid growing market and the applications of automated health care systems are reviewed in the paper. The paper also provides information about the demand and implementation of such technology in various sectors of medical and pharmaceutical industry like lab automation, hospital management, nanomedicine and human genome project. The effect of the regulatory, ethical and quality context in the implementation of new technologies are explained to highlight the standardizations to be adhered in this sector in comparison to any other domain. The paper also highlights the current practices healthcare automation like Robot surgeries and Pharmacy Automation system. The application of Industrial engineering concepts are also reviewed focusing on application of Lean management system, Six Sigma, Simulation and Modeling techniques in healthcare to improve overall efficiency, system performance and achieve better customer satisfaction.

A vocabulary development and visualization tool based on natural language processing and the mining of textual patient reports

Medical terminologies are critical for automated healthcare systems. Some terminologies, such as the UMLS and SNOMED are comprehensive, whereas others specialize in limited domains (i.e., BIRADS) or are developed for specific applications. An important feature of a terminology is comprehensive coverage of relevant clinical terms and ease of use by users, which include computerized applications. We have developed a method for facilitating vocabulary development and maintenance that is based on utilization of natural language processing to mine large collections of clinical reports in order to obtain information on terminology as expressed by physicians. Once the reports are processed and the terms structured and collected into an XML representational schema, it is possible to determine information about terms, such as frequency of occurrence, compositionality, relations to other terms (such as modifiers), and correspondence to a controlled vocabulary. This paper describes the method and discusses how it can be used as a tool to help vocabulary builders navigate through the terms physicians use, visualize their relations to other terms via a flexible viewer, and determine their correspondence to a controlled vocabulary.

Using Nursing Expertise for Non-Nursing Computer Systems

The nurse working with automated systems often uses the same practical and clinical skills used in the analysis, implementation, and evaluation of patient care. Transferring those cognitive skills from a clinical setting to a setting in which automated systems are the primary focus is practical, yet challenging. The intuitive processes of analysis, implementation, and evaluation are the same; however, the methods, procedures, and tools vary. The additional skills required to become a systems analyst, technical writer, information specialist, project officer, or consultant can be learned either formally by attending courses, seminars, workshops, or other educational programs or by on-the-job experience with the many aspects of systems implementation. Whenever possible, opportunities for learning should be sought by the nurse to develop her analytical and technical skills, depending on the nature of the role she fills in working with automated systems. Experience with non-nursing technology provides the nurse with a deeper understanding and appreciation of the applications of computer technology in a variety of settings. This kind of knowledge contributes to her ability to more accurately assess the capabilities desirable in automated health care system, with particular emphasis on nursing requirements. The combination of nursing, technical, and analytical skills places the nurse working with automated systems in a position to greatly expand her knowledge base and to identify mechanisms to transfer non-nursing technology to automation in nursing practice, education, and research. The challenge to the nursing profession will continue as automation continues to become an integral part of health care delivery.(ABSTRACT TRUNCATED AT 250 WORDS)