Antonija Krstačić

Contrast Set Mining for Distinguishing Between Similar Diseases

The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of contrast set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.

Heart rate variability and nonlinear dynamic analysis in patients with stress-induced cardiomyopathy

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 long-range 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.

Non-linear analysis of heart rate variability in patients with coronary heart disease

The article emphasizes clinical and prognostic significance of non-linear measures of the heart rate variability, applied on the group of patients with coronary heart disease (CHD) and age-matched healthy control group. Three different methods were applied: Hurst exponent (H), Detrended Fluctuation Analysis (DFA) and approximate entropy (ApEn). Hurst exponent of the R-R series was determined by the range rescaled analysis technique. DFA was used to quantify fractal long-range-correlation properties of heart rate variability. Approximate entropy measures the unpredictability of fluctuations in a time series. It was found that the short-term fractal scaling exponent (/spl alpha//sub 1/) is significantly lower in patients with CHD (0.93 /spl plusmn/ 0.07 vs. 1.09 /spl plusmn/ 0.04; p < 0.001). The patients with CHD had lower Hurst exponent in each program of exercise test separately, as well as approximate entropy than healthy control group (P < 0.001).

Contrast set mining through subgroup discovery applied to brain ischaemina data

Contrast set mining aims at finding differences between different groups. This paper shows that a contrast set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to contrast set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.

CSM-SD: Methodology for contrast set mining through subgroup discovery

This paper addresses a data analysis task, known as contrast set mining, whose goal is to find differences between contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning settings, a one-versus-all and a pairwise contrast set mining setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered contrast sets can be improved by offering additional contrast set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.

Clinical data analysis based on iterative subgroup discovery: experiments in brain ischaemia data analysis

Abstract This paper presents a case study of the process of insightful analysis of clinical data collected in regular hospital practice. The approach is applied to a database describing patients suffering from brain ischaemia, either permanent as brain stroke with positive computer tomography (CT) or reversible ischaemia with normal brain CT test. The goal of the analysis is the extraction of useful knowledge that can help in diagnosis, prevention and better understanding of the vascular brain disease. This paper demonstrates the applicability of subgroup discovery for insightful data analysis and describes the expert’s process of converting the induced rules into useful medical knowledge. Detection of coexisting risk factors, selection of relevant discriminative points for numerical descriptors, as well as the detection and description of characteristic patient subpopulations are important results of the analysis. Graphical representation is extensively used to illustrate the detected dependencies in the available clinical data.

Some important R-R interval based paroxysmal atrial fibrillation predictors

A trial fibrillation is the most common sustained cardiac arrhythmia. The result of series of machine learning experiments is detection of some promising paroxysmal atrial fibrillation predictors. Based on ratio of short and long R-R intervals there is a possibility to generate rules for PAF screening and predicting. For PAF screening the calculated ratio were 2.00 for successive R-R intervals. The problem of imminent PAF prediction is much more difficult and the concept of normalisation hail to be implemented. The optimal seems to be ratio between the shortest and the longest R-R interval, which was at least 1.75 times larger than ratio during the normalisation time for the same patient. Also it was detected that maximal distance of the longest and the shortest R-R intervals should be up to six R-R intervals.

The Chaos Theory and Non-linear Dynamics in Heart Rate Variability in Patients with Heart Failure

This study evaluate and quantify the non-linear dynamic changes of heart rate variability based on ldquochaos theoryrdquo and fractal mathematics in 250 patients with heart failure during 12 months. Some different non-linear methods were applied: fractal dimension (FD), detrented fluctuation analysis (DFA) and approximate entropy (ApEn). Fractal correlation properties and fractal dimension in this study may reflect altered neuroanatomic interaction that may predispose to the development of severe HF. It was found that the short-term fractal scaling exponent (alpha1) is significantly lower in patients with HF. The patients with HF had also lower approximate entropy and higher fractal dimension with positive impact of modern HE therapy.

Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data

The goal of exploratory pattern mining is to find patterns that exhibit yet unknown relationships in data and to provide insightful representations of detected relationships. This paper explores contrast set mining and an approach to improving its explanatory potential by using the so called supporting factors that provide additional descriptions of the detected patterns. The proposed methodology is described in a medical data analysis problem of distinguishing between similar diseases in the analysis of patients suffering from brain ischaemia.

Hereditary multiple exostoses: an unusual cause of spinal cord compression

We report on a 19-year-old boy with hereditary multiple exostoses (HME) who presented several months history of progressive weakness of his left hand and left lower limb. He walked unsteadily and with difficulty. A detailed neurological examination revealed the power of 2/5 in the left hand, 3/5 in the left lower extremity, and 4/5 in the right limbs. There was significant hypoesthesia over left hand and left lower extremity, as well as a loss of position and vibration senses on sensory examination. Deep tendon reflexes were hyperactive in both lower extremities. There were clonus, Hoffman, and Babinski signs bilaterally. Magnetic resonance imaging (MRI) is recommended. MRI demonstrates an exophytic, lobulated bone mass extending from the left aspect of the C6 vertebral body, including left pedicle and left facet joint C6–C7 measuring 3 9 5.0 9 2.7 cm. The lesion has a heterogeneous signal and demonstrates an extensive displacement of the surrounding paravertebral soft tissues and nerve root and entering and occupying the spinal canal, thus causing a significant contra lateral displacement of the medulla (Fig. 1). Computed tomography (CT) of cervical spine through the area demonstrates a large solitary osteochondroma arising from the left side low-cervical spine area with bulging into the vertebral canal (Fig. 2). The patient was surgically treated for intraspinal exostoses and showed cessation of neurological deficits (Fig. 3). HME is an autosomal dominant inherited musculoskeletal disorder with a wide spectrum of clinical manifestations. It is characterized by the development of benign tumors, multiple osteochondromas (exostoses), growing outward from the metaphyses of long bones. Multiple osteochondromatosis is the most common of the bone dysplasia, but neurological complications are rare [1]. The number of osteochondromas, and the number and location of involved bones vary. Spinal cord compression, however, is a very rare entity in patients with HME. Between 1 and 4 % of solitary osteochondromas arise in the spine, and 7–9 % of patients with HME develop a spinal lesion. The cervical, thoracic, and lumbar regions can be affected. Lesions mostly originate from posterior vertebral elements, less often lesions causing neural compression originate from the vertebral bodies. Spinal osteochondromas usually cause a variety of signs and symptoms, including those of spinal cord or root compression. These are the result of progressive encroachment of the slowly expanding mass on neural structures [2, 3]. Cervical spinal cord compression resulting from osteochondroma is an extremely serious complication of HME. The incidence of malignant transformation into chondrosarcoma is reported to be between 5 and 11 % [4]. & Antonija Krstačić akrstacic@gmail.com