Abstract
Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen-Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.

Abstract
A large amount of information is continuously generated in intensive health care. An analysis of these data streams can supply valuable insights to improve the monitoring of the patients. The volume, frequency and complexity of data, which come unlabeled, make their analysis a challenging task. Machine learning (ML) techniques have been successfully employed for mining data streams to extract useful knowledge for health care monitoring. It includes the detection of changes in the behavior of sensors, failures on machines or systems, and data anomalies. Anomaly (or outlier) detection is a ML task that aims to find exceptions or abnormalities in a dataset. These exceptions, in a medical context, can represent a new disease pattern, an event to be further investigated, behavior changes or potential health complications. Despite of its analysis in data streams is a challenging task, temporal abstractions techniques should help due to they deal with the management and abstraction of time based data, offering high level of visualization of each data object in its context. The aim of this paper is to review recent research in anomaly detection and temporal abstractions and discuss the application of their combination to intensive care data streams.

Abstract
The incidence of colorectal cancer cases in the Portuguese Institute of Oncology of Porto created the need of a telenursing program in the Gastro-Intestinal Cancer Unit. After staging, treatment may involve surgery radio and chemotherapy (either oral or IV). Patients with no treatment after surgery are scheduled for medical exams every 3 months in the first 2 years. Patients on chemotherapy need to be compliant and to have a close monitoring of adverse events. The GI Cancer Unit uses a telenursing information system to help assess colorectal cancer patients' follow-up after surgery, medical treatment compliance and adverse events. A mixed-methods evaluation was done to a) describe the target population, b) detect problems in the telenursing information system, and c) suggest changes to meet users' requirements. From 181 outbound phone calls, representing 67 patients (49 in treatment and 18 in follow-up), patients' main characteristics were extracted and system's problems were identified by the intervening nurses. Recommendations will be useful for a further development of the system.

Abstract
Objectives: This article seeks to identify neuroanatomical differences in ADHD through an overview of systematic reviews that report encephalic differences compared to a control group in volume, area, activation likelihood or chemical composition.Methods: We conducted a systematic search using Cochrane guidelines and PRISMA criteria in PubMed, Scopus, Web of Science, Cochrane Database of Systematic Reviews and Database of Abstracts of Reviews of Effects.Results: Results revealed broad encephalic involvement that includes a functional frontal and cingulate hypoactivation and structural differences in corpus callosum, cerebellum and basal nuclei.Conclusions: ADHD symptoms might be due to a multi-network unbalanced functioning hypothesis.

Abstract
Obstructive Sleep Apnea (OSA) affects 2-4% of the population worldwide. The standard test for OSA diagnosis is polysomnography (PSG), an expensive exam limited to urban areas. Furthermore, nearly half of all PSG tests results are negative for OSA. This work aims to reduce these unnecessary exams, by defining an auxiliary diagnostic method that could be used to assess patient's need for PSG, according to their probability of OSA diagnosis. A prospective study was conducted on adult patients with OSA suspicion who performed PSG at our sleep laboratory in Portugal. The studied clinical variables were defined after literature review and collected during consultation. Two comparable cohorts were studied for derivation (n=86) and validation (n=33) of models. Three classifiers were analyzed - a multiple logistic regression classifier (AUC=80.0%) and two Bayesian networks classifiers - Naive Bayes (AUC=81.3%) and Tree Augmented Naive Bayes (TAN, AUC=81.4%) - aiming at the best possible specificity (identification of unnecessary exams). Overall, sensitivity-adjusted models could detect normal patients, preventing unnecessary PSG, while keeping sensitivity high. Furthermore, the graphical representation of TAN can be explored by the physician during consultation, making it a helpful tool to assess patients' need to perform PSG.

Abstract
A previous version of this article contained minor errors in Tables 2, 3 and 4. This has now been corrected, the publisher apologises for the error. © 2016 European Crohn's and Colitis Organisation (ECCO).