GDV technique and machine learning: Current research and results

We use machine learning to analyze GDV images of leaves of apple trees and human fingers. We are interested in two hypotheses: 1. GDV images of plant leaves contain information about plant condition, 2. Human bioelectromagnetic field can be influenced by outside factors, such as vitalized water from special glasses. We performed four independent studies: (a) analyzing coronas of apple leaves, (b) detecting the effect of K2000 glasses on human BEM field, (c) detecting the effect of mobile phones on human BEM field and (d) detecting the effect of energized orbs on human BEM field

Prilagodljivi računalniški sistem za priporočanje učnih objektov v konstruktivističnem učnem okolju – ALECA

Today there are increasingly more learning environments which support active learning, taking into account student characteristics, preferences and activities. In this paper, we present a concept of a learning recommender system, which combines knowledge from pedagogy and recommending systems. We analyse the influence of combining different learning styles models on preferred types of multimedia materials. The results reveal that students prefer well-structured learning texts with color discrimination, and that the hemispheric learning style model is the most important criterion in determining student preferences for different multimedia learning materials. In the second part of our research, we describe an approach to alleviating the new user problem in terms of better recommendation accuracy of the system for recommending learning materials in environments where the system has no prior information about learners. Our findings present the concept of an adaptive learning system, with an analysis of its possible effects in learning practice.Dandanes se pojavlja vse več učnih sistemov, ki podpirajo aktivno učenje in upoštevajo učenčeve učne lastnosti, značilnosti in aktivnosti. V prispevku predstavljamo zasnovo učnega priporočilnega sistema, ki združuje znanja pedagogike in računalniških priporočilnih algoritmov. Proučujemo, kako združevanje modelov učnih stilov vpliva na izbiro različnih tipov večpredstavnih učnih gradiv. Rezultati kažejo, da študentje za učenje najpogosteje uporabljajo dobro strukturirana učna gradiva, ki vsebujejo barvno diskriminacijo, in da je hemisferični model učnih stilov najpomembnejši odločitveni kriterij. V nadaljevanju opisujemo postopek za reševanje t. i. problema hladnega zagona, s katerim je mogoče izboljšati točnost sistema za priporočanje učnih gradiv v okoljih, kjer o učencih nimamo predhodnih podatkov. Namen prispevka je predstaviti idejno zasnovo prilagodljivega učnega sistema z analizo njegovih predvidenih učinkov na učno prakso

Cardiopulmonary assessment of patients diagnosed with Gaucher’s disease type I

Background: Understanding the basis of the phenotypic variation in Gaucher's disease (GD) has proven to be challenging for efficient treatment. The current study examined cardiopulmonary characteristics of patients with GD type 1. Methods: Twenty Caucasian subjects (8/20 female) with diagnosed GD type I (GD-S) and 20 age- and sex-matched healthy controls (C), were assessed (mean age GD-S: 32.6 ± 13.1 vs. C: 36.2 ± 10.6, p >.05) before the initiation of treatment. Standard echocardiography at rest was used to assess left ventricular ejection fraction (LVEF) and pulmonary artery systolic pressure (PASP). Cardiopulmonary exercise testing (CPET) was performed on a recumbent ergometer using a ramp protocol. Results: LVEF was similar in both groups (GD-S: 65.1 ± 5.2% vs. C: 65.2 ± 5.2%, p >.05), as well as PAPS (24.1 ± 4.2 mmHg vs. C: 25.5 ± 1.3 mmHg, p >.05). GD-S had lower weight (p <.05) and worse CPET responses compared to C, including peak values of heart rate, oxygen consumption, carbondioxide production (VCO2), end-tidal pressure of CO2, and O2 pulse, as well as HR reserve after 3 min of recovery and the minute ventilation/VCO2 slope. Conclusions: Patients with GD type I have an abnormal CPET response compared to healthy controls likely due to the complex pathophysiologic process in GD that impacts multiple systems integral to the physiologic response to exercise

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general

Estimation of individual prediction reliability using sensitivity analysis of regression models

The dissertation discusses the reliability estimation of individual regression predictions. In contrast with average measures for the evaluation of model accuracy, the reliability estimates for individual predictions can provide additional information which could be beneficial for evaluating the usefullness of the prediction (medical diagnosis, financial and control applications). As a novelty, the dissertation proposes a method for reliability estimation of predictions, which is based on the sensitivity analysis approach and is independent of the regression model. New reliability estimates are compared with traditional or adapted reliability estimates. The problem of optimal reliability estimate selection based on the given problem domain and the regression model was also studied using metalearning and internal cross-validation approach. The testing was performed with 8 regression models, with larger number of benchmark problem domains and in a real domain from the area of medical prognostics. The results showed the potential of the proposed methodology in practice