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1.
Heart rate variability and nonlinear dynamic analysis in patients with stress-induced cardiomyopathy
Antonija Krstačić, Paolo Castiglioni, Dragan Gamberger, Gianfranco Parati, Goran Krstačić, Robert Steiner, 2012, izvirni znanstveni članek

Opis: Complexity-based analyses may quantify abnormalities in heart rate variability (HRV). The aim of this study was to investigate the clinical and prognostic significances of dynamic HRV changes in patients with stress-induced cardiomyopathy Takotsubo syndrome (TS) by means of linear and nonlinear analysis. Patients with TS were included in study after complete noninvasive and invasive cardiovascular diagnostic evaluation and compared to an age and gender matched control group of healthy subjects. Series of R–R interval and of ST–T interval values were obtained from 24-h ECG recordings after digital sampling. HRV analysis was performed by ‘range rescaled analysis’ to determine the Hurst exponent, by detrended fluctuation analysis to quantify fractal longrange correlation properties, and by approximate entropy to assess time-series predictability. Short- and long-term fractal-scaling exponents were significantly higher in patients with TS in acute phases, opposite to lower approximate entropy and Hurst exponent, but all variables normalized in a few weeks. Dynamic HRV analysis allows assessing changes in complexity features of HRV in TS patients during the acute stage, and to monitor recovery after treatment, thus complementing traditional ECG and clinically analysis.
Najdeno v: osebi
Ključne besede: Heart rate variability, Nonlinear dynamics, Chaos theory, Stress-induced cardiomyopathy, Takotsubo syndrome
Objavljeno: 13.07.2017; Ogledov: 1866; Prenosov: 0
.pdf Polno besedilo (742,92 KB)

2.
Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms
Johannes Fuernkranz, Nada Lavrač, Dragan Gamberger, 2010, samostojni znanstveni sestavek ali poglavje v monografski publikaciji

Opis: Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that separation of the feature construction and rule construction processes has theoretical and practical justification. Explicit usage of features enables a unifying framework of both propositional and relational rule learning and we present and analyze procedures for feature construction in both types of domains. It is demonstrated that the presented procedure for constructing a set of simple features has the property that the resulting set enables construction of complete and consistent rules whenever it is possible, and that the set does not include obviously irrelevant features. Additionally, the concept of feature relevancy is important for the effectiveness of rule learning. It this work, we illustrate the concept in the coverage space and prove that the relative relevancy has the quality-preserving property in respect to the resulting rules. Moreover, we show that the transformation from the attribute to the feature space enables a novel, theoretically justified way of handling unknown attribute values. The same approach enables that estimated imprecision of continuous attributes can be taken into account, resulting in construction of robust features in respect to this imprecision.
Najdeno v: osebi
Ključne besede: Machine learning, Feature construction, Rule learning, Unknown attribute values
Objavljeno: 14.07.2017; Ogledov: 1777; Prenosov: 0
.pdf Polno besedilo (365,76 KB)

3.
Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease
Dragan Gamberger, Nada Lavrač, Shantanu Srivatsa, Rudolph E. Tanzi, Murali Doraiswamy, 2017, izvirni znanstveni članek

Opis: The heterogeneity of Alzheimer’s disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.
Najdeno v: osebi
Ključne besede: Alzheimer's disease, Rapid decliners, Data clustering, Mild cognitive impairment
Objavljeno: 17.08.2017; Ogledov: 1743; Prenosov: 173
.pdf Polno besedilo (1,78 MB)

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