In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification.

Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized.

Many symptoms of neuropsychiatric disorders such as tinnitus are subjective and variable over time. Typically, patients are asked to report symptoms, their severity, and duration retrospectively, e.g., in interviews or self-report questionnaires. However, little is known on how well such retrospective reports correspond with the experience of the symptoms at the moment they occurred in daily life. Mobile technologies can help in that end: mobile self-help services allow patients to record their symptoms prospectively, when (or short after) they occur in daily life.

Hospital Puerta del Hierro in Madrid, Spain, implemented in November 2011 a new service that aims to aid the patients of the oncology service with their doubts during their treatments through the use of a centralized call center. This service was created with the aim of provide a more personalized patient attention as well as to try to reduce the number of re-entries in the hospital in the emergencies.

Subgroup discovery (SD) exploits its full value in applications where the goal is to generate understandable models. Epidemiologists search for statistically significant relationships between risk factors and outcome in large and heterogeneous datasets encompassing information about the participants' health status gathered from questionnaires, medical examinations and image acquisition. SD algorithms can help epidemiologists by automatically detecting such relationships presented as comprehensible rules, aiming to ultimately improve prevention, diagnosis and treatment of diseases.

In Obstructive Sleep Apnea, respiratory effort is maintained but ventilation decreases/disappears because of the partial/total occlusion in the upper airway. It affects about 4% of men and 2% of women in the world population. The aim was to define an auxiliary diagnostic method that can support the decision to perform polysomnography (standard test), based on risk and diagnostic factors. Our sample performed polysomnography between January and May of 2015.

Numeracy skill level of patients has great influence on their preferences and priorities for the treatment options concerning their healthcare. There have been different methods for assessment of numeracy skill in healthcare domain. In our previous research we proposed a new Confidence-based Patient Numeracy Assessment (C-PNA) method. In this paper we compare it with other numeracy assessment methods in terms of newly proposed quality characteristics.

This paper highlights the challenges for researchers when undertaking research on multidisciplinary medical teams (MDTs) in real-world healthcare settings, and suggests ways in which these challenges may be addressed.

The attractiveness and robustness of graph theory has stimulated an unprecedented increase in studies aiming to understand the functional organization and dynamics of the brain. The investigation of brain connectomics produces a tremendous amount of data which may be examined at both a macroscopic and microscopic level. However, the interpretation of findings is still challenging. Novel methodological approaches should enhance the arsenal of the tools employed towards the understanding of the interaction of the distinct brain components.

Alcohol abuse disorder or alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioural impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for detecting and classifying alcoholism. Many brain disorders could be detected by analyzing the Electroencephalography (EEG) signals. In this paper, for extracting the required markers we analyze the EEG signals for two groups of alcoholic and control subjects.

P300 speller systems represent one of the most basic applications of Brain-Computer Interfaces (BCIs). A traditional P300 speller consists of a 6 by 6 grid of characters in which each column or row in this grid intensifies at random. During such intensification process, the electroencephalography (EEG) data of the subject is recorded and analyzed to determine the character to be spelled.