Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort.

Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers.

Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease.

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.

The Influence of Corticosteroids on Heart Rate Variablity in Acute Cervical Spinal Cord Injury.

Heart rate variability (HRV) gives information on the sympathetic-parasympathetic autonomic balance. The aim of the study was to analyze sympathovagal balance after acute spinal cord injury (SCI), demonstrated by linear measures in time and frequency domain of HRV and to analyze the effect of corticosteroids on HRV parameters in SCI. The study included 40 tetraplegic patients with acute SCI and 40 healthy subjects as control group.

Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

This paper presents homogeneous clusters of patients, identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer's disease patient groups.

Homogeneous clusters of Alzheimer's disease patient population.

Background: Identification of biomarkers for the Alzheimer's disease (AD) is a challenge and a very difficult task both for medical research and data analysis.

Methods: We applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors.

Results: The main result is identification of three clusters of patients with significant problems with dementia.

Control of heart rate by the autonomic nervous system in acute spinal cord injury.

Spinal cord injury may cause loss of cardiovascular reflexes mediated by sympathetic drive due to interruption in the supraspinal control of spinal sympathetic motoneurons. The aim of this study was to analyze sympathovagal balance after acute spinal cord injury demonstrated by linear measures in time and frequency domain of heart rate variability (HRV). The study included 40 tatraplegic patients after acute spinal cord injury and 40 healthy subjects as controls.

A novel way of integrating rule-based knowledge into a web ontology language framework.

Web ontology language (OWL), used in combination with the Protégé visual interface, is a modern standard for development and maintenance of ontologies and a powerful tool for knowledge presentation. In this work, we describe a novel possibility to use OWL also for the conceptualization of knowledge presented by a set of rules. In this approach, rules are represented as a hierarchy of actionable classes with necessary and sufficient conditions defined by the description logic formalism.

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.

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.

Semantic web ontology utilization for heart failure expert system design.

In this work we present the usage of semantic web knowledge representation formalism in combination with general purpose reasoning for building a medical expert system. The properties of the approach have been studied on the example of the knowledge base construction for decision support tasks in the heart failure domain. The work consisted of descriptive knowledge presentation in the ontological form and its integration with the heart failure procedural knowledge.

Evaluation of artificial intelligence based models for chemical biodegradability prediction.

This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models.

[Arterial hypertension in Croatia. Results of EH-UH study].

Arterial hypertension (AH) is the most important independent risk factor of cardiovascular diseases. The prevalence of AH is higher than it was several decades before, and in Europe it is approximately 40%. A higher prevalence of AH has been reported in Europe than in the United States and Canada.

[Treatment and control of hypertension in Croatia. The BEL-AH study].

Poor control of blood pressure (BP) is one of the main reasons for high cardiovascular mortality and morbidity. The aim of this study was to analyse control of BP in outpatient settings in four biggest towns in Croatia. The study included 412 medical doctors (GP) and 7031 middle-aged patients (62.

HEARTFAID: A knowledge based platform of services for supporting medical-clinical management of heart failure within elderly population.

HEARTFAID is a research and development project aimed at devising, developing and validating an innovative knowledge based platform of services, able to improve early diagnosis and to make more effective the medical-clinical management of heart diseases within elderly population. Chronic Heart Failure is one of the most remarkable health problems for prevalence and morbidity, especially in the developed western countries, with a strong impact in terms of social and economic effects. All these aspects are typically emphasized within the elderly population, with very frequent hospital admissions and a significant increase of medical costs.

Induction of comprehensible models for gene expression datasets by subgroup discovery methodology.

Finding disease markers (classifiers) from gene expression data by machine learning algorithms is characterized by a high risk of overfitting the data due the abundance of attributes (simultaneously measured gene expression values) and shortage of available examples (observations). To avoid this pitfall and achieve predictor robustness, state-of-the-art approaches construct complex classifiers that combine relatively weak contributions of up to thousands of genes (attributes) to classify a disease. The complexity of such classifiers limits their transparency and consequently the biological insights they can provide.

Data mining server--on-line knowledge induction tool.

The aim of this paper is to present an on-line data mining tool and illustrate its use on example of real medical data. Data from the Laboratory for in-vitro Thyroid diagnostics at the Sisters of Charity University Hospital in Zagreb were used. Preparation of the data set and one session of knowledge induction is described.

Active subgroup mining: a case study in coronary heart disease risk group detection.

This paper presents an approach to active mining of patient records aimed at discovering patient groups at high risk for coronary heart disease (CHD). The approach proposes active expert involvement in the following steps of the knowledge discovery process: data gathering, cleaning and transformation, subgroup discovery, statistical characterization of induced subgroups, their interpretation, and the evaluation of results. As in the discovery and characterization of risk subgroups, the main risk factors are made explicit, the proposed methodology has high potential for patient screening and early detection of patient groups at risk for CHD.

Evaluation of phenotypic characteristics for differentiation of enterococcal species using an example based algorithm.

A computer based rule-generation system of Inductive Learning by Logic Minimization (ILLM) was used to determine the sufficient set of biochemical reactions and necessary conditions that have to be fulfilled for correct differentiation of enterococci recovered from humans. The simplest combination of physiological tests for differentiation Enterococcus faecalis from all other enterococcal species consisted of only 3 reactions. Reactions that tested the ability of acidification D-xylose, mannitol, L-arabinose and Na-pyruvate were useful for delineation of both E.

[Selection of phenotype characteristics in differentiation of various species of enterococci using an inductive mathematical program].

In this paper a novel computer example-based learning system (Inductive learning by logic minimization) was used to determine the sufficient set of biochemical reactions and necessary conditions that have to be fulfilled for the correct identification of enterococci isolated from human specimens. Several combinations for accurate identification of Enterococcus faecalis and Enterococcus feacium from other enterococci were found. The simplest combination set for E.

ILLM--inductive learning algorithm as a method for prediction of life expectancy achieving.

The aim of the paper was to use inductive learning algorithm ILLM in the field of epidemiology, for prediction the life expectancy achieving. Data base comprised results of epidemiological investigation in some regions of Croatia. Overall accuracies for different samples and corresponding rules produced by ILLM algorithm, showed some interesting results.

Applications of experts' judgement to derive structure-biodegradation relationships.

The experts' judgement data on microbial degradation were used to develop the first general QSAR biodegradability model (Boethling and Sabljic, 1989) which is composed of a set of structural descriptors and a set of quantitative rules. Its evaluation and validation with experimental biodegradation data clearly show that the developed model gives a realistic and reliable account of structurebiodegradability relationship for organic chemicals. The same set of experts judgement data was used to develop structure-biodegradation rule by the application of an inductive machine learning method.