Conceptually Funded Usability Evaluation of an Application for Leveraging Descriptive Data Analysis Models for Cardiovascular Research.

The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie's layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists' research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie's layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings.

Detection of emotion by text analysis using machine learning.

Emotions are an integral part of human life. We know many different definitions of emotions. They are most often defined as a complex pattern of reactions, and they could be confused with feelings or moods. They are the way in which individuals cope with matters or situations that they find personally significant. Emotion can also be characterized as a conscious mental reaction (such as anger or fear) subjectively experienced as a strong feeling, usually directed at a specific object. Emotions can be communicated in different ways. Understanding the emotions conveyed in a text or speech of a human by a machine is one of the challenges in the field of human-machine interaction. The article proposes the artificial intelligence approach to automatically detect human emotions, enabling a machine (i.e., a chatbot) to accurately assess emotional state of a human and to adapt its communication accordingly. A complete automation of this process is still a problem. This gap can be filled with machine learning approaches based on automatic learning from experiences represented by the text data from conversations. We conducted experiments with a lexicon-based approach and classic methods of machine learning, appropriate for text processing, such as Naïve Bayes (NB), support vector machine (SVM) and with deep learning using neural networks (NN) to develop a model for detecting emotions in a text. We have compared these models' effectiveness. The NN detection model performed particularly well in a multi-classification task involving six emotions from the text data. It achieved an F1-score = 0.95 for sadness, among other high scores for other emotions. We also verified the best model in use through a web application and in a Chatbot communication with a human. We created a web application based on our detection model that can analyze a text input by web user and detect emotions expressed in a text of a post or a comment. The model for emotions detection was used also to improve the communication of the Chatbot with a human since the Chatbot has the information about emotional state of a human during communication. Our research demonstrates the potential of machine learning approaches to detect emotions from a text and improve human-machine interaction. However, it is important to note that full automation of an emotion detection is still an open research question, and further work is needed to improve the accuracy and robustness of this system. The paper also offers the description of new aspects of automated detection of emotions from philosophy-psychological point of view.

Explainability of deep learning models in medical video analysis: a survey.

Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine and have been performing significantly better in comparison to other traditional machine learning methods. However, the black-box nature of deep neural networks has restricted their use in real-world applications, especially in healthcare. Therefore, explainability of the machine learning models, which focuses on providing of the comprehensible explanations of model outputs, may affect the possibility of adoption of such models in clinical use. There are various studies reviewing approaches to explainability in multiple domains. This article provides a review of the current approaches and applications of explainable deep learning for a specific area of medical data analysis-medical video processing tasks. The article introduces the field of explainable AI and summarizes the most important requirements for explainability in medical applications. Subsequently, we provide an overview of existing methods, evaluation metrics and focus more on those that can be applied to analytical tasks involving the processing of video data in the medical domain. Finally we identify some of the open research issues in the analysed area.

The possible role of machine learning in detection of increased cardiovascular risk patients - KSC MR Study (design).

Introduction: Currently, just a few major parameters are used for cardiovascular (CV) risk quantification to identify many of the high-risk subjects; however, they leave a lot of them with an underestimated level of CV risk which does not reflect the reality. Material and methods: The submitted study design of the Kosice Selective Coronarography Multiple Risk (KSC MR) Study will use computer analysis of coronary angiography results of admitted patients along with broad patients' characteristics based on questionnaires, physical findings, laboratory and many other examinations. Results: Obtained data will undergo machine learning protocols with the aim of developing algorithms which will include all available parameters and accurately calculate the probability of coronary artery disease. Conclusions: The KSC MR study results, if positive, could establisha base for development of proper software for revealing high-risk patients, as well as patients with suggested positive coronary angiography findings, based on the principles of personalised medicine.

Analysis of Risk Factors in Patients with Subclinical Atherosclerosis and Increased Cardiovascular Risk Using Factor Analysis.

Today, there are many parameters used for cardiovascular risk quantification and to identify many of the high-risk subjects; however, many of them do not reflect reality. Modern personalized medicine is the key to fast and effective diagnostics and treatment of cardiovascular diseases. One step towards this goal is a better understanding of connections between numerous risk factors. We used Factor analysis to identify a suitable number of factors on observed data about patients hospitalized in the East Slovak Institute of Cardiovascular Diseases in Kosice. The data describes 808 participants cross-identifying symptomatic and coronarography resulting characteristics. We created several clusters of factors. The most significant cluster of factors identified six factors: basic characteristics of the patient; renal parameters and fibrinogen; family predisposition to CVD; personal history of CVD; lifestyle of the patient; and echo and ECG examination results. The factor analysis results confirmed the known findings and recommendations related to CVD. The derivation of new facts concerning the risk factors of CVD will be of interest to further research, focusing, among other things, on explanatory methods.

Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients.

(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to separate different stages of mild cognitive impairment (MCI) and physical frailty into clusters; the differences were assessed by using a multinomial logistic regression model. (3) Results: Four clusters (latent classes) were identified: (1) highly functional (the mean and SD of the "frailty" test 0.58 ± 0.72 and the Mini-Mental State Examination (MMSE) test 27.42 ± 1.5), (2) cognitive impairment (0.97 ± 0.78 and 21.94 ± 1.95), (3) cognitive frailty (3.48 ± 1.12 and 19.14 ± 2.30), and (4) physical frailty (3.61 ± 0.77 and 24.89 ± 1.81). (4) Discussion: The comorbidity patterns distinguishing the clusters depend on the degree of development of cardiometabolic disorders in combination with advancing age. The physical frailty phenotype is likely to exist separately from the cognitive frailty phenotype and includes common musculoskeletal diseases.

Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders.

BACKGROUND Physical frailty, cognitive impairment, and symptoms of anxiety and depression frequently co-occur in later life, but, to date, each has been assessed separately. The present study assessed their patterns in primary care patients aged ≥60 years. MATERIAL AND METHODS This cross-sectional study evaluated 263 primary care patients aged ≥60 years in eastern Croatia in 2018. Physical frailty, cognitive impairment, anxiety and depression, were assessed using the Fried phenotypic model, the Mini-Mental State Examination (MMSE), the Geriatric Anxiety Scale (GAS), and the Geriatric Depression Scale (GDS), respectively. Patterns were identified by latent class analysis (LCA), Subjects were assorted by age, level of education, and domains of psychological and cognitive tests to determine clusters. RESULTS Subjects were assorted into four clusters: one cluster of relatively healthy individuals (61.22%), and three pathological clusters, consisting of subjects with mild cognitive impairment (23.95%), cognitive frailty (7.98%), and physical frailty (6.85%). A multivariate, multinomial logistic regression model found that the main determinants of the pathological clusters were increasing age and lower mnestic functions. Lower performance on mnestic tasks was found to significantly determine inclusion in the three pathological clusters. The non-mnestic function, attention, was specifically associated with cognitive impairment, whereas psychological symptoms of anxiety and dysphoria were associated with physical frailty. CONCLUSIONS Clustering of physical and cognitive performances, based on combinations of their grades of severity, may be superior to modelling of their respective entities, including the continuity and non-linearity of age-related accumulation of pathologic conditions.

Case-Based Reasoning for Support of the Diagnostics of Cardiovascular Diseases.

In this paper we present a decision support system, which has been designed and implemented on the case-based reasoning principles. Our decision support system is being implemented in tight cooperation with the cardiologist, who represents the main future users of the system. Our system enables its user to find the most similar historical cases to a new patient, suggest the most probable result of the potential coronary angiography examination and also provide various useful visualizations to the cardiologist, who is responsible for the final decision about recommending the coronary angiography or not for the new patient. The first response from the cardiologist about our system is very promising.

Seroprevalence of hepatitis E among hospitalized patients in Slovakia: first report.

OBJECTIVE: Hepatitis E infection is one of the most frequent acute hepatitis in the world. Currently five human genotypes with different geographical distributions and distinct epidemiologic patterns are identified. In Slovakia, only rare cases of hepatitis E have been reported in recent years. Therefore, the aim of the study was to evaluate the prevalence of anti-HEV total antibodies and the main risk factors for HEV in the general population in Eastern Slovakia. METHODS: Detection of anti-HEV total antibodies samples was done by a commercial enzyme-linked immunosorbent assay (ELISA) kit. RESULTS: Of 175 hospitalized patients included in the study, 76 (43.5%) showed positivity for anti-HEV total antibodies. No statistically significant differences were found in anti-HEV positivity between men and women or in the groups of different living areas (town/village - urban/rural). CONCLUSION: Prevalence of anti-HEV total antibodies of hospitalised patients was high. The risk factor significantly associated with antibody positivity was eating raw meat. Other factors, such as sex, age, living area and contact with animals were not associated with antibody positivity.

Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records.

BACKGROUND: There is potential for medical research on the basis of routine data used from general practice electronic health records (GP eHRs), even in areas where there is no common GP research platform. We present a case study on menopausal women with hypertension and metabolic syndrome (MS). The aims were to explore the appropriateness of the standard definition of MS to apply to this specific, narrowly defined population group and to improve recognition of women at high CV risk. METHODS: We investigated the possible uses offered by available data from GP eHRs, completed with patients interview, in goal of the study, using a combination of methods. For the sample of 202 hypertensive women, 47-59 years old, a data set was performed, consisted of a total number of 62 parameters, 50 parameters used from GP eHRs. It was analysed by using a mixture of methods: analysis of differences, cutoff values, graphical presentations, logistic regression and decision trees. RESULTS: The age range found to best match the emergency of MS was 51-55 years. Deviations from the definition of MS were identified: a larger cut-off value of the waist circumference measure (89 vs 80 cm) and parameters BMI and total serum cholesterol perform better as components of MS than the standard parameters waist circumference and HDL-cholesterol. The threshold value of BMI at which it is expected that most of hypertensive menopausal women have MS, was found to be 25.5. The other best means for recognision of women with MS include triglycerides above the threshold of 1.7 mmol/L and information on statins use. Prevention of CVD should focus on women with a new onset diabetes and comorbidities of a long-term hypertension with anxiety/depression. CONCLUSIONS: The added value of this study goes beyond the current paradigm on MS. Results indicate characteristics of MS in a narrowly defined, specific population group. A comprehensive view has been enabled by using heterogenoeus data and a smart combination of various methods for data analysis. The paper shows the feasibility of this research approach in routine practice, to make use of data which would otherwise not be used for research.

Medical Data Analytics Is Not a Simple Task.

Data analytics represents a new chance for medical diagnosis and treatment to make it more effective and successful. This expectation is not so easy to achieve as it may look like at a first glance. The medical experts, doctors or general practitioners have their own vocabulary, they use specific terms and type of speaking. On the other side, data analysts have to understand the task and to select the right algorithms. The applicability of the results depends on the effectiveness of the interactions between those two worlds. This paper presents our experiences with various medical data samples in form of SWOT analysis. We identified the most important input attributes for the target diagnosis or extracted decision rules and analysed their interestingness with cooperating doctors, for most promising new cut-off values or an investigation of possible important relations hidden in data sample. In general, this type of knowledge can be used for clinical decision support, but it has to be evaluated on different samples, conditions and ideally in long-term studies. Sometimes, the interaction needed much more time than we expected at the beginning but our experiences are mostly positive.