Abstract
Obstructive Sleep Apnea (OSA) is a disease that affects approximately 4% of men and 2% of women worldwide but is still underestimated and underdiagnosed. The standard method for assessing this index, and therefore defining the OSA diagnosis, is polysomnography (PSG). Previous work developed relevant Bayesian network models but those were based only on variables univariatedly associated with the outcome, yielding a bias on the possible knowledge representation of the models. The aim of this work was to develop and validate new Bayesian network decision support models that could be used during sleep consult to assess the need for PSG. Bayesian models were developed using a) expert opinion, b) hill-climbing, c) naive Bayes and d) TAN structures. Resulting models validity was assessed with in-sample AUC and stratified cross-validation, also comparing with previously published model. Overall, models achieved good discriminative power (AUC>70%) and validity (measures consistently above 70%). Main conclusions are a) the need to integrate a wider range of variables in the final models and b) the support of using Bayesian networks in the diagnosis of obstructive sleep apnea.

Abstract
Clinical record integration and visualisation is one of the most important abilities of modern health information systems (HIS). Its use on clinical encounters plays a relevant role in the efficacy and efficiency of health care. One solution is to consider a virtual patient record (VPR), created by integrating all clinical records, which must collect documents from distributed departmental HIS. However, the amount of data currently being produced, stored and used in these settings is stressing information technology infrastructure: integrated VPR of central hospitals may gather millions of clinical documents, so accessing data becomes an issue. Our vision is that, making clinical reports to be stored either in primary (fast) or secondary (slower) storage devices according to their likelihood of visualisation can help manage the workload of these systems. The aim of this work was to develop a model that predicts the probability of visualisation, within 24h after production, of each clinical report in the VPR, so that reports less likely to be visualised in the following 24 hours can be stored in secondary devices. We studied log data from an existing virtual patient record (n=4975 reports) with information on report creation and report first-time visualisation dates, along with contextual information. Bayesian network classifiers were built and compared with logistic regression, revealing high discriminating power (AUC around 90%) and accuracy in predicting whether a report is going to be accessed in the 24 hours after creation.

Abstract
Crohn's disease is one type of inflammatory bowel disease whose incidence is currently increasing, and may affect any part of both the small and large intestine, possibly irritating deeper layers of the organs. Being a chronic disease, neither treatment nor surgery actually heals the patients. Thus, focus has been given to identifying good prognostic models based on clinical factors since they are more easily included in daily practice. The aim of this work is to provide an initial study on the adequacy of a Bayesian network model to enhance the prognosis prediction for patients with Crohn's disease. Multicentric study data of patients with surgery or immunosuppression in the six month after diagnosis was used to derive a Bayesian network, focusing on the prognosis and the analysis of factors interaction, including clinical features, disease course, treatment, follow-up plan, and adverse events. Two models were evaluated (naive Bayes and Tree-Augmented Naive Bayes) and also compared with logistic regression, using cross-validation and ROC curve analysis. Preliminary results showed competitive accuracy (above 75%) and discriminative power (above 70%). The generated models presented interesting insights on factor interaction and predictive ability for the prognosis, supporting their use in future clinical decision support systems.

Abstract
Introduction: Colectomy is a major event that may significantly affect the outcome of ulcerative colitis (UC) in terms of both quality of life and mortality. This paper aims to identify clinical prognostic factors that may be significantly associated with this event. Methods: PubMed, ISI Web of Knowledge and Scopus were searched to identify studies investigating the association between clinical factors in adult patients with UC and studied events. The clinical factors evaluated in this meta-analysis were gender, smoking habits, disease extent, use of corticosteroids, and episodes of hospitalization. Results: Of the 3753 initially selected papers, 20 were included. The analysis showed a significantly lower risk of colectomy for female patients (odds ratio [OR] 0.78 [95% CI 0.68, 0.90]) and for smoking patients (OR 0.55 [0.33, 0.91]), and a higher risk for patients with extensive disease (OR 3.68 [2.39, 5.69]), for patients who took corticosteroids at least once (OR 2.10 [1.05, 4.22]), and for patients who were hospitalized (OR 4.13 [3.23, 5.27]). Conclusion: Gender, smoking habits, disease extent, need for corticosteroids, and hospitalization were all significantly associated with UC prognosis. These results may clarify the relative influences of these and other prognostic factors in the natural course of the disease and therefore help improve the management approach, thus improving the follow-up of patients.

Abstract
Background: Mammography is considered the best imaging technique for breast cancer screening, and the radiographer plays an important role in its performance. Therefore, continuing education is critical to improving the performance of these professionals and thus providing better health care services. Objective: Our goal was to develop an e-learning course on breast imaging for radiographers, assessing its efficacy, effectiveness, and user satisfaction. Methods: A stratified randomized controlled trial was performed with radiographers and radiology students who already had mammography training, using pre-and post-knowledge tests, and satisfaction questionnaires. The primary outcome was the improvement in test results (percentage of correct answers), using intention-to-treat and per-protocol analysis. Results: A total of 54 participants were assigned to the intervention (20 students plus 34 radiographers) with 53 controls (19+ 34). The intervention was completed by 40 participants (11+ 29), with 4 (2+ 2) discontinued interventions, and 10 (7+ 3) lost to follow-up. Differences in the primary outcome were found between intervention and control: 21 versus 4 percentage points (pp), P<. 001. Stratified analysis showed effect in radiographers (23 pp vs 4 pp; P=. 004) but was unclear in students (18 pp vs 5 pp; P=. 098). Nonetheless, differences in students' posttest results were found (88% vs 63%; P=. 003), which were absent in pretest (63% vs 63%; P=. 106). The per-protocol analysis showed a higher effect (26 pp vs 2 pp; P<. 001), both in students (25 pp vs 3 pp; P=. 004) and radiographers (27 pp vs 2 pp; P<. 001). Overall, 85% were satisfied with the course, and 88% considered it successful. Conclusions: This e-learning course is effective, especially for radiographers, which highlights the need for continuing education.

Abstract
In year 2015, we experience a proliferation of scientific publications, conferences and funding programs on KDD for medicine and healthcare. However, medical scholars and practitioners work differently from KDD researchers: their research is mostly hypothesis-driven, not data-driven. KDD researchers need to understand how medical researchers and practitioners work, what questions they have and what methods they use, and how mining methods can fit into their research frame and their everyday business. Purpose of this tutorial is to contribute to this learning process. We address medicine and healthcare; there the expertise of KDD scholars is needed and familiarity with medical research basics is a prerequisite. We aim to provide basics for (1) mining in epidemiology and (2) mining in the hospital. We also address, to a lesser extent, the subject of (3) preparing and annotating Electronic Health Records for mining.