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
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).

Abstract
Bayesian networks are one of the most intuitive statistical models for both estimation, classification and prediction of patients' outcomes. However, the availability of inference software in clinical settings is still limited. This work presents preliminary steps towards the creation of simple web-based forms that can access a powerful Bayesian network inference engine, making the derived models usable at bedside by both the clinicians and the patients themselves.

Abstract
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 2015. Two Bayesian classifiers were used to build the models: Naive Bayes (NB) and Tree augmented Naive Bayes (TAN), using all 39 variables or just a selection of 13. Area under the ROC curve, sensitivity, specificity, predictive values were evaluated using cross-validation. From a collected total of 241 patients, only 194 fulfill the inclusion criteria. 123 (63%) were male, with a mean age of 58 years old. 66 (34%) patients had a normal result and 128 (66%) a diagnostic of obstructive sleep apnea. The AUCs for each model were: NB39 - 72%; TAN39 - 79%; NB13 - 75% and TAN13 - 75%. The high (34%) proportion of normal results confirm the need for a pre-evaluation prior to polysomnography. The constant seeking of a validated model to screen patients with suspicion of obstructive sleep apnea is essential, especially at the level of primary care.

Abstract

Abstract
In pharmacovigilance, reported cases are considered suspected adverse drug reactions (ADR). Health authorities have thus adopted structured causality assessment methods, allowing the evaluation of the likelihood that a medicine was the causal agent of an adverse reaction. The aim of this work was to develop and validate a new causality assessment support system used in a regional pharmacovigilance centre. A Bayesian network was developed, for which the structure was defined by an expert, aiming at implementing the current guidelines for causality assessment, while the parameters were learnt from 593 completely-filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre expert between 2000 and 2012. Precision, recall and time to causality assessment (TTA) was evaluated, according to the WHO causality assessment guidelines, in a retrospective cohort of 466 reports (April to September 2014) and a prospective cohort of 1041 reports (January to December 2015). Results show that the network was able to easily identify the higher levels of causality (recall above 80%), although strugling to assess reports with a lower level of causality. Nonetheless, the median (Q1:Q3) TTA was 4 (2:8) days using the network and 8 (5:14) days using global introspection, meaning the network allowed a faster time to assessment, which has a procedural deadline of 30 days, improving daily activities in the centre.