Intelligent Healthcare System Research Articles

Towards for Designing Intelligent Health Care System Based on Machine Learning

Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people’s health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people’s lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.

A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing.

Coronavirus (COVID-19) is a very contagious infection that has drawn the world’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data’s intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system’s robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion.

Realistic medical image super-resolution with pyramidal feature multi-distillation networks for intelligent healthcare systems.

There are two key requirements for medical lesion image super-resolution reconstruction in intelligent healthcare systems: clarity and reality. Because only clear and real super-resolution medical images can effectively help doctors observe the lesions of the disease. The existing super-resolution methods based on pixel space optimization often lack high-frequency details which result in blurred detail features and unclear visual perception. Also, the super-resolution methods based on feature space optimization usually have artifacts or structural deformation in the generated image. This paper proposes a novel pyramidal feature multi-distillation network for super-resolution reconstruction of medical images in intelligent healthcare systems. Firstly, we design a multi-distillation block that combines pyramidal convolution and shallow residual block. Secondly, we construct a two-branch super-resolution network to optimize the visual perception quality of the super-resolution branch by fusing the information of the gradient map branch. Finally, we combine contextual loss and L1 loss in the gradient map branch to optimize the quality of visual perception and design the information entropy contrast-aware channel attention to give different weights to the feature map. Besides, we use an arbitrary scale upsampler to achieve super-resolution reconstruction at any scale factor. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance compared to other methods in this work.

Remote Intelligent Healthcare System Based on the ROCKET Technique

Remote Intelligent Healthcare System Based on the ROCKET Technique

Programming Language based Doctor Less Corona Patient Treatment as well as Monitoring

Coronavirus (COVID-19) is an infectious disease whisch split human to human caused by a newly acquired coronavirus. Mostly people infected with COVID-19 will experience moderate respiratory illness and recover without the need for special treatment. Older people, as well as those with less medical problems such as disease,diabetes, chronic respiratory diseases, and cancer are more likely to be seriously ill. The best way to prevent and reduce transmission of COVIT-19Protect yourself and others from infection by washing your hands or using alcohol-based medicine often and without touching your face. The COVID-19 virus is spread mainly through saliva droplets or runny nose when an infected person coughs or sneezes, so it is important that you re-practice the practice of breathing (for example, by coughing on a flexible elbow). At this time, there are no specific drugs or specifications for COVID-19. However, there are many ongoing clinical trials examining possible treatments. We can only protect ourselves from this problem because we currently do not have a cure so in this proposed program we will provide a setup that will enable automatic protection through the installation of equipment and set up electrical equipment. In India, about 200 percent of people lose their lives as a result of disrupted health care systems which means that in many hospitals, the doctor visits patients with a morning shift or in the afternoon shift or on individual shifts. What happens if a patient's health becomes important during this time or when the doctor is not in the market with the patient. The solution is; the patient may lose his or her life. thus avoiding this important situation; we tend to suggest a device that raises the device of an intelligent embedded system that monitors the health of patients indefinitely. This system monitors patients' heart rate, vital signs, and salt water (if any). If any of the above parameters are on the far side of the periphery, this sensible device informs physicians or caregivers and suggests ways to improve the patient's overall health savings. Objects (IoT) sees the way forward to something in any place by anyone at any time. information and communication technology helps to transform digital technologies. The IoT location unit is designed to connect multiple mobile devices to networks. reveals the structural design of an intelligent health care system using the Wireless Sensor Network. Monitoring a patient's health is a common function in health care facilities from home to hospital. In the proposed system, patients carry a set of nerve endings to collect their physical limits. Arduino is attached to the patient's body, assists the deaf and sends sensory information to the server via WiFi. WiFi, which is used in most hospital systems, provides very little interference with the operation of some devices. The server detects unusual cases of patients using the limit value and sends an SMS and email to the doctor with a video-feed. The system allows for the movement of a patient wearing a nerve and the video feed enhances physician communication. Through this program we can improve the quality of treatment for patients who may need ongoing long-term health monitoring.

A smart service warehousing platform supporting big data deep learning modeling analysis

Chronic disease management is the most expensive, fastest growing and most difficult problem for medical care workers in various countries. Current Health care information systems do not have interoperability characteristics and lack of data model standards, which makes it very difficult to extract meaningful information for further analysis. Deep learning can help medical care giver analyze various features of collecting data of patients and possibly more accurately diagnose and improve medical treatment through early detection and prevention. Our approach uses P4 medical model, which is predictive, preventative, personalized and participatory, which identifies diseases at early stage of diseases development, therefore it helps patients improve their daily behavior and health status. In this paper, an effective and reliable intelligent service warehousing platform, which is a service framework and a middle layer, is designed to maintain the quality of service of the intelligent health care system and to analyze and design to predict the risk factors that contribute to diabetes and kidney disease. The mathematical prediction model is provided to doctors to support their patient’s treatment. At the end we verified the availability and effectiveness of this service platform from the data of hospital.

Intellectual systems in the management of medical technologies and quality of life

The article is devoted to the development and use of intelligent systems in the management of medical technological processes and health-related quality of life (HRQOL). The relevance of the work is due to the need for effective use of intelligent systems in healthcare. The purpose of this work is to study the possibilities and prospects of using information technologies and artificial intelligence systems in clinical medicine to improve the efficiency of providing medical care to the population. Information retrieval method; theoretical analysis of legislative and regulatory documents, literary sources, Internet resources, research results; spectral-dynamic and mathematical analysis of the current state and assessment of the quality of life of an individual using the artificial intelligence system "CME". The paper analyzes the development trends of information technologies and artificial intelligence systems, as well as the features of their use in medical technological processes. As an example, the technological capabilities of the intelligent system Complex Medical Expert are briefly described.

Prediction of Treatment Medicines With Dual Adaptive Sequential Networks

Predicting treatment medicines is a key task in many intelligent healthcare systems. Prediction of treatment medicines can assist doctors in making informed prescription decisions for patients according to their Electronic Health Records (EHRs). However, predicting treatment medicines is a challenging task due to the following reasons: (1) heterogeneous nature of EHR data that typically includes laboratory results, treatment records, disease conditions, and demographic information; (2) complex correlations among EHR sequences, including inter-correlations between sequences and temporal intra-correlations within each sequence; (3) temporal dynamics of these correlations changing with disease progression. In this paper, we predict treatment medicines for patients with dual adaptive sequential networks (DASNet). Specifically, DASNet is designed with three components. First, a decomposed adaptive long short-term memory network (DA-LSTM) is designed to capture the intra- and inter-correlations in multiple heterogeneous temporal sequences. Then, we develop an attentive meta learning network (AT-MetaNet) to learn dynamic weight parameters for DA-LSTM, thus enabling it to model various correlation structures. Finally, we employ an attentive fusion network (AT-FuNet) to incorporate historical information and collectively fuse representation embeddings of heterogeneous data to predict treatment medicines. Our results on the public MIMIC-III dataset covering 11 medical conditions demonstrate that the proposed end-to-end model can achieve the state-of-the-art prediction performance while providing clinically useful insights.

An intelligent healthcare system for predicting and preventing dengue virus infection

Dengue is a mosquito-borne pandemic viral infection, which transmits to humans from Female Aedes albopictis or Aedes agypti mosquitoes. It progressively deteriorates the health of infected individuals and poses a high threat of human morbidity and mortality. This paper proposes an intelligent healthcare system which identifies, monitors, and alerts dengue virus (DeV) infected individuals and other stakeholders in real-time and control the DeV infection outbreak using cloud computing, internet of things and fog computing paradigms. The proposed system uses Naive Bayesian Network (NBN) for diagnosing the possibly DeV infected individuals and generating real-time alerts for suggesting and alerting the concerned stakeholders for taking on-time necessary actions at the fog subsystem. The proposed system also uses Social Network Analysis at the cloud subsystem, to provide Global Positioning Systems (GPS)-based global risk assessment of the DeV infection on Google Maps (Google-based web map service) and control DeV infection outbreak. The analysis of the experimental results acknowledges the efficiency of the NBN-based DeV infection diagnosis, alert generation, and GPS-based risk assessment functionality, of the proposed system, via various statistical measures and experimental approaches.

An Anonymous Certificateless Signcryption Scheme for Internet of Health Things

Internet of Health Things (IoHT) is a hot topic of research presently, which provides a reliable and intelligent healthcare system for monitoring the physical conditions of the patients over the Internet from anywhere and anytime. The ease of time-independent interaction from geographically remote areas is a core advantage of the IoHT system, which offers preventive or proactive healthcare facilities at a lower cost. IoHT communication, on the other hand, is usually carried out with a range of low-power biomedical sensors, rendering them vulnerable to cyber-attacks and incompatible with traditional cryptographic techniques. The most critical security concern in IoHT is ensuring the authenticity of patients’ health-related messages sent over the internet. Other key concerns include receiver anonymity and forward security, which means that only the sender knows the identities of the recipients. As a result, even if the private key of senders compromised, the adversary will be unable to decrypt the ciphertext. Existing signcryption schemes that employ certificateless cryptography for the healthcare system failed to guarantee both receiver anonymity and forward security simultaneously. Therefore, in this article, we propose an anonymous certificateless signcryption scheme for IoHT applications, which is based on the notion of the Hyperelliptic Curve (HEC) cryptosystem to satisfy these security requirements. The proposed scheme guarantees formal security analysis for confidentiality, unforgeability, and receiver anonymity using the Random Oracle Model (ROM). The results authenticate that the proposed scheme improves security while lowering computation and communication costs.

Parallel versus cascaded logistic regression trained single-hidden feedforward neural network for medical data

ObjectiveAn important step towards a better healthcare system is fast and accurate diagnosis. In the last decade, the application of intelligent systems in healthcare has led to impressive results. The goal of this paper is to extend the LogSLFN (single-hidden layer feedforward neural network trained using logistic regression) algorithm, which has been deployed successfully in the past for the case of a two-class decision problem, to the case of multiple classes. We have considered and statistically analyzed two approaches: a parallel LogSLFN, and a cascaded LogSLFN. Materials and methodsAccording to the universal approximation theorem, a single-hidden layer feedforward neural network has the ability to approximate arbitrarily closely continuous functions of several real variables under certain reasonable assumptions. Essentially, a single hidden layer containing a finite fixed number of neurons is sufficient to provide an arbitrarily well approximation to a given training set of inputs and a desired target output represented by a continuous function. Parallel LogSLFN and cascaded LogSLFN are two novel approaches that can be applied to multiple-class decision problems. Both methods are extensions of the LogSLFN, which uses logistic regression to compute the weights between the input and hidden layer of a single-hidden layer feedforward network. No error correction is needed, the weights between the hidden and the output layer being computed using the Moore-Penrose pseudoinverse matrix. The proposed models have been tested on two medical datasets regarding cancer diagnosis and liver fibrosis staging. Experimental results and the subsequent statistical analysis have proved the robustness of the proposed models with other machine learning techniques reported in literature. Main findingsThe experimental results showed that the Parallel approach surpasses the Cascaded one. Still, both models are competitive to the other state-of-the-art techniques. ConclusionsThe LogSFLN algorithm can be successfully extended to multiple-class decision problems. By embedding knowledge extracted from the data into the architecture, we obtained a raise by 20% in accuracy when applied on the liver fibrosis dataset.

An Intelligent IoT Based Healthcare System Using Fuzzy Neural Networks

Healthcare facilities in modern age are key challenge especially in developing countries where remote areas face lack of high-quality hospitals and medical experts. As artificial intelligence has revolutionized various fields of life, health has also benefited from it. The existing architecture of store-and-forward method of conventional telemedicine is facing some problems, some of which are the need for a local health center with dedicated staff, need for medical equipment to prepare patient reports, time constraint of 24–48 hours in receiving diagnosis and medication details from a medical expert in a main hospital, cost of local health centers, and need for Wi-Fi connection. In this paper, we introduce a novel and intelligent healthcare system that is based on modern technologies like Internet of things (IoT) and machine learning. This system is intelligent enough to sense and process a patient’s data through a medical decision support system. This system is low-cost solution for the people of remote areas; they can use it to find out whether they are suffering from a serious health issue and cure it accordingly by contacting near hospitals. The results of the experiments also show that the proposed system is efficient and intelligent enough to provide health facilities. The results presented in this paper are the proof of the concept.

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation.

The coronavirus disease 2019 (COVID-19) pandemic has resulted in public health interventions such as physical distancing restrictions to limit the spread and transmission of the novel coronavirus, causing significant effects on the delivery of physical healthcare procedures worldwide. The unprecedented pandemic spurs strong demand for intelligent robotic systems in healthcare. In particular, medical telerobotic systems can play a positive role in the provision of telemedicine to both COVID-19 and non-COVID-19 patients. Different from typical studies on medical teleoperation that consider problems such as time delay and information loss in long-distance communication, this survey addresses the consequences of physiological organ motion when using teleoperation systems to create physical distancing between clinicians and patients in the COVID-19 era. We focus on the control-theoretic approaches that have been developed to address inherent robot control issues associated with organ motion. The state-of-the-art telerobotic systems and their applications in COVID-19 healthcare delivery are reviewed, and possible future directions are outlined.

An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment.

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms.

Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.

A deep learning approach for facility patient attendance prediction based on medical booking data

Nowadays, data-driven methodologies based on the clinical history of patients represent a promising research field in which personalized and intelligent healthcare systems can be opportunely designed and developed. In this perspective, Machine Learning (ML) algorithms can be efficiently adopted to deploy smart services to enhance the overall quality of healthcare systems. In this work, starting from an in-depth analysis of a data set composed of millions of medical booking records collected from the public healthcare organization in the region of Campania, Italy, we have developed a predictive model to extract useful knowledge on patients, medical staff, and related healthcare structures. In more detail, the main contribution is to suggest a Deep Learning (DL) methodology able to predict the access of a patient in one or more medical facilities of a fixed set in the immediate future, the subsequent 2 months. A structured Temporal Convolutional Neural Network (TCNN) is designed to extract temporal patterns from the administrative medical history of a patient. The experiment shows the goodness of the designed methodology. Finally, this work represents a novel application of a TCNN model to a multi-label classification problem not linked to text categorization or image recognition.

Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

Automatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.

Development of intelligent healthcare system based on ambulatory blood pressure measuring device

Currently, the market size of blood pressure monitors both in domestic and overseas is gradually increasing due to the increase in hypertension patients resulting from aging population. In addition, the necessity of developing systems and devices for the healthcare of hypertension patients is also increasing. Moreover, the determination of health normality in respect to the management of hypertension patients is possible, but it is essentially important to incorporate preventive healthcare. Thus, further studies on deep learning-based prediction technology using previous data are needed. This paper proposes the development of an intelligent healthcare management system that can help to manage the health of hypertensive patients. The system includes a wrist-worn ambulatory blood pressure monitoring device that can analyze the normality of measured blood pressures. The performance evaluation results of the proposed system verified the reliability of data acquisition as compared with the existing equipment as well as the efficiency of the intelligent healthcare system.

Nurse Robots: A Necessity in the Nursing Care System during the Covid-19 Pandemic

At present, the use of nursing robots as one of the intelligent health care systems to infection control and maintain is necessary to prevention and control of Covid-19. These robots can be used to perform repetitive, tedious and dangerous activities that expose nurses to the Covid-19 such as delivering food to the patient, measuring vital signs, disinfecting the environments, collecting hazardous waste, tracking patients and carriers and monitoring the quarantined environments. Also, nasopharyngeal and oropharyngeal swab sampling and monitored changing in patients' blood parameters can be performed by nursing robots and significantly reduce the transmission of infection and promote the occupational safety .The use of nursing robots has solved the ergonomic problems of nurses, including the problem of moving people and bearing their weight. One of the most common hospital wastes during the Covid-19 crisis is plastic masks, gloves and protective clothing, that their use is inevitable in this crisis. Using robot nursing lead to decrease in environmental pollution. Lack of spiritual and friendly communication with the patient and weakness in providing services to patients who are not alert are among the current limitations of robots. Robots cannot replace nurses, but can be their assistants in the difficult days of dealing with crises such as Covid-19.Although the providing of robots incurs costs for governments, their useful and diverse applications in crises, as well as their durability as part of essential medical equipment, justify their presence in health care systems.Covid-19 pandemic is a challenge that provides an opportunity to identify new solutions to overcome such biological crises. The outbreak of this challenging and deadly epidemic has shown that focusing on hospital smartening is not a luxury and unnecessary thinking, but futurism and investing in maintaining the health of the community and health professionals.

An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set.

Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system. A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pre-trained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of the proposed method. The method concentrates on histopathological images to classify the breast cancer. The performance is compared with the state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.