Towards a decision support system for disorders of the cardiovascular system diagnosing and evaluation of the treatment efficiency

The study describes a preliminary stage of the decision support system development for cardiovascular system disorders. As the clinical model of the disorders, the arterial hypertension was used. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recording while performing tilt-test functional load. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate a treatment efficiency. The results indicate the potential of the proposed decision support system. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserve

Artificial Intelligence for Hospital Health Care:Application Cases and Answers to Challenges in European Hospitals

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 138 reports, articles, and other documents introduced into the NASA scientific and technical information system in Jun. 1980

Detecting Heart Attacks Using Learning Classifiers

Cardiovascular diseases (CVDs) have emerged as a critical global threat to human life. The diagnosis of these diseases presents a complex challenge, particularly for inexperienced doctors, as their symptoms can be mistaken for signs of aging or similar conditions. Early detection of heart disease can help prevent heart failure, making it crucial to develop effective diagnostic techniques. Machine Learning (ML) techniques have gained popularity among researchers for identifying new patients based on past data. While various forecasting techniques have been applied to different medical datasets, accurate detection of heart attacks in a timely manner remains elusive. This article presents a comprehensive comparative analysis of various ML techniques, including Decision Tree, Support Vector Machines, Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting, Multilayer Perceptron, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. These classifiers are implemented and evaluated in Python using data from over 300 patients obtained from the Kaggle cardiovascular repository in CSV format. The classifiers categorize patients into two groups: those with a heart attack and those without. Performance evaluation metrics such as recall, precision, accuracy, and the F1-measure are employed to assess the classifiers’ effectiveness. The results of this study highlight XGBoost classifier as a promising tool in the medical domain for accurate diagnosis, demonstrating the highest predictive accuracy (95.082%) with a calculation time of (0.07995 sec) on the dataset compared to other classifiers

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits

Key Trends In Digital Health And The Future Of Clinical Trials In The Us

With the increasing burden of chronic diseases on the global population, many stakeholders see digital health technologies and devices as potential solutions to improve patient self-management of their disease and offer novel treatment methods. Digital health solutions including mobile apps, web-based programs, texting, and connected devices have been applied to a wide variety of diseases. In recent years, interest in digital health technologies has exploded with almost 200 digital health related articles published in PubMed in 2019 alone. In particular, digital health holds great potential in improving and enhancing the traditional clinical trial by increasing patient recruitment and retention and introducing novel assessment and collection methods that shift clinical trials from the physical site to the patients’ home. Digital health is poised to fundamentally shift how clinical trials are conducted. However, serious challenges from potential regulatory restrictions and data privacy issues will need to be addressed before patients, physicians, and other stakeholders can fully realize the benefits of digital health

What does it take to make integrated care work? A ‘cookbook’ for large-scale deployment of coordinated care and telehealth

The Advancing Care Coordination & Telehealth Deployment (ACT) Programme is the first to explore the organisational and structural processes needed to successfully implement care coordination and telehealth (CC&TH) services on a large scale. A number of insights and conclusions were identified by the ACT programme. These will prove useful and valuable in supporting the large-scale deployment of CC&TH. Targeted at populations of chronic patients and elderly people, these insights and conclusions are a useful benchmark for implementing and exchanging best practices across the EU. Examples are: Perceptions between managers, frontline staff and patients do not always match; Organisational structure does influence the views and experiences of patients: a dedicated contact person is considered both important and helpful; Successful patient adherence happens when staff are engaged; There is a willingness by patients to participate in healthcare programmes; Patients overestimate their level of knowledge and adherence behaviour; The responsibility for adherence must be shared between patients and health care providers; Awareness of the adherence concept is an important factor for adherence promotion; The ability to track the use of resources is a useful feature of a stratification strategy, however, current regional case finding tools are difficult to benchmark and evaluate; Data availability and homogeneity are the biggest challenges when evaluating the performance of the programmes