The Internet of Things (IoT) has enabled the invention of smart health monitoring systems. These health monitoring systems can track a person’s mental and physical wellness. Stress, anxiety, and hypertension are key causes of many physical and mental disorders. Age‐related problems such as stress, anxiety, and hypertension necessitate specific attention in this setting. Stress, anxiety, and blood pressure monitoring can prevent long‐term damage by detecting problems early. This will increase the quality of life and reduce caregiver stress and healthcare costs. Determine fresh technology solutions for real‐time stress, anxiety, and blood pressure monitoring using discreet wearable sensors and machine learning approaches. This study created an automated artefact detection method for BP and PPG signals. It was proposed to automatically remove outlier points generated by movement artefacts from the blood pressure signal. Next, eleven features taken from the oscillometric waveform envelope were utilised to analyse the relationship between diastolic blood pressure (SBP) and systolic blood pressure (DBP). This paper validates a proposed computational method for estimating blood pressure. The proposed architecture leverages sophisticated regression to predict systolic and diastolic blood pressure values from PPG signal characteristics.

The purpose of this paper, acceptance of Electronic Health Records (EHRs) system in the Jordan eHealth sector, is to contribute to develop the quality of hospitals, reduce medical error issues and reduce health costs. However, few researches have addressed the antecedent factors of healthcare professionals’ intentions to use an EHR system. Founded on trust factors integrated with the UTAUT2 model, a theoretical model is proposed to explain the exercise behaviour of healthcare professionals to use an EHR system.

Of the most popular applications of artificial intelligence (AI), those used in the health sector are the ones that represent the largest proportion, in terms of use and expectation. An investigative systematization model is proposed in the scientific training of nursing professionals, by articulating epistemological positions from previous studies on the subject. In order to validate the model proposed, a prototype was created to present an application that could help nurses in their clinical processes, storing their experiences in a case base for future research. The prototype consisted of digitizing paediatric nursing diagnoses and inserting them into a case base in order to assess the effectiveness of the prototype in handling these cases in a structure conducive to retrieval, adaptation, indexing, and case comparison. This work presents as a result a computational tool for the health area, employing one of the artificial intelligence techniques, case-based reasoning (CBR). The small governmental nursing education institution in Bangladesh used in this study did not yet have the systemization of nursing care (NCS) and computerized support scales.

A cross-sectional study of patients with suspected diabetic retinopathy (DR) who had an ophthalmological examination and a retinal scan is the focus of this research. Specialized retinal images were analyzed and classified using OPF and RBM models (restricted Boltzmann machines). Classification of retinographs was based on the presence or absence of disease-related retinopathy (DR). The RBM and OPF models extracted 500 and 1000 characteristics from the images for disease classification after the system training phase for the recognition of retinopathy and normality patterns. There were a total of fifteen different experiment series, each with a repetition rate of 30 cycles. The study included 73 diabetics (a total of 122 eyes), with 50.7% of them being men and 49.3% being women. The population was on the older side, at 59.7 years old on average. The RBM-1000 had the highest overall diagnostic accuracy (89.47) of any of the devices evaluated. The RBM-500 had a better autodetection system for DR signals in fundus images than the competition (100% sensitivity). In terms of specificity, RBM-1000 and OPF-1000 correctly identified all of the images that lacked DR signs. In particular, the RBM model of machine learning automatic disease detection performed well in terms of diagnostic accuracy, sensitivity, and application in diabetic retinopathy screening.

Cancer is a noncommunicable chronic disease that indistinctly affects people of any nationality, race, ethnicity, age, or social class. Because of its unpredictability, receiving the diagnosis of this disease is almost always alarming for the patient. Breast cancer, especially among women, occupies a prominent position in this ranking. However, if diagnosed early, there is an excellent chance of a cure. In this sense, digital technologies have been advancing at an increasingly fast pace to support the early diagnosis of the disease. Clinical analysis of breast cancer is commonly performed using diagnostic imaging. One of the most used exams considered the main one for the early detection of this type of cancer is mammography. This exam allows the visualization of breast tissue from image screening using X‐rays. In this sense, the use of computational techniques is essential to assist medical professionals in diagnosing this disease, thus making prevention and early diagnosis even more effective in the current context. The present work is limited to the use of digital technologies (image processing and artificial intelligence) that cooperate with the early diagnosis of breast cancer, which supports the medical professional to analyze images and be able to diagnose, from an early stage, the emergence of breast cancer of the disease, significantly increasing the chances of curing it. It can be gathered from this research how the discovery of X‐rays and the growth in this sector combined with cutting‐edge technology have benefitted in the early detection of the disease and even offered the cure of many cases.

This study focuses on hybrid synchronization, a new synchronization phenomenon in which one element of the system is synced with another part of the system that is not allowing full synchronization and nonsynchronization to coexist in the system. When , where Y and X are the state vectors of the drive and response systems, respectively, and Wan ( = 1)), the two systems’ hybrid synchronization phenomena are realized mathematically. Nonlinear control is used to create four alternative error stabilization controllers that are based on two basic tools: Lyapunov stability theory and the linearization approach.