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Abstract
Background Ulcerative colitis [UC] is a chronic inflammatory disease often accompanied by severe and distressing symptoms that, in some patients, might require a surgical intervention [colectomy]. This study aimed at determining the risk of experiencing progressive disease or requiring colectomy. Material and Methods This was a multicentre study: patients' data [n = 1481] were retrieved from the Portuguese database of inflammatory bowel disease patients. Bayesian networks and logistic regression were used to build risk matrices concerning the outcomes of interest. Results The derivation cohort included a total of 1210 patients, of whom 6% required a colectomy and 37% had progressive disease [over a median follow-up period of 12 syears]. The risk matrices show that previously hospitalised patients with extensive disease, who are not on immunomodulators and who are refractory to corticosteroid treatment, are the ones at the highest risk of undergoing a colectomy [88%]; whereas male patients, with extensive disease and less than 40 years old at diagnosis, are the ones at the highest risk of experiencing progressive disease [72%]. These results were internally and externally validated, and the AUC [area under the curve] of the ROC [receiver operating characteristic] analysis for the derivation cohort yielded a high discriminative power [92% for colectomy and 72% for progressive disease]. Conclusions This study allowed the construction of risk matrices that can be used to accurately predict a UC patient's likelihood of requiring a colectomy or of facing progressive disease, and can be used to individualise therapeutic strategies.

Abstract
Healthcare institutions face serious security challenges, namely confidentiality, integrity and availability of patient's data due to the amounts of sensitive data collected on Health Information Systems (HIS) and the complex data management processes in health care. This paper describes scenarios of undue HIS access by staff in healthcare institutions, use cases (UC) that model the activities on HIS and identify the variables on audit trails (AT) logs that can be used to detect illegitimate actions on patients' data. Firstly, a survey was conducted through discussion meetings with Information Systems Director (ISD), Data Protection Officer (DPO) and a jurist to discuss their concerns about patient data access, followed by interviews to professionals from healthcare institutions to gather information about their routines and HIS access practices. Then, undue access scenarios were described and UC of activities on HIS which allow their detection were modelled. Lastly, necessary log variables were identified in order to produce algorithms for illegitimate accesses detection. UC and variables selected were matched with the specific requirements of Ministers Council Resolution (MCR) nr.41/2018 which provides guidelines for technology to be compliant with General Data Protection Regulations (GDPR). Discussions with ISD, DPO and the jurist, and professionals' interviews allowed us to describe nine scenarios of undue access. For each scenario we modelled one UC. 32 variables from different type of logs were identified for illegitimate access detection, of which 14 are mandatory according to MCR nr. 41/2018. Despite we might have some limitations related to poor HIS log quality, the mandatory data that logs must comply will be very useful for the development of UC presented. In addition, it is possible to request systems' vendors the improvement of logs' data to meet the detail we propose for this model, which may be very useful to comply not only with GDPR requirements but also with the Standard "Management of Information" (MOI.11) of Joint Commission International Standards for Hospitals (JCI) certification. As future work, we intend to develop the algorithms for the UC modelled, that will detect suspicious activities and produce alarmistic in their presence, testing them in real environment of a hospital to help Information Systems department and DPO on investigation and prevention of data breaches. (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
Purpose: E-learning has been revealed as an a useful tool among continuing education within health professionals, namely for radiographers or radiologic technologists. Therefore like traditional learning, this teaching approach needs continuous evaluation in order to validate its effectiveness and impact. Kirkpatrick's model has been widely used for this purpose by health information management instructors. Our aim was to assess an E-learning Course on Breast Imaging for radiographers based on the first three levels of Kirkpatrick's framework: reaction, learning and behaviour. Methods and materials: An E-learning course was developed for radiographers in order to provide an easy-to-understand, succinct and current overview in breast imaging, namely mammography technique and image interpretation. The program structure were built based on the guidelines proposed by the European Society of Breast Cancer Specialists (EUSOMA). Learner's satisfaction was assessed through a questionnaire and Knowledge gain was assessed using pre- and post-testing. After 6 months of complying the course, the learners were contacted through a questionnaire in order to give feedback on whether their behaviour changed in workplace. Results: Two editions of the breast imaging course were performed by 64 learners. In general, 97% of the learners stated that the program content was very good and excellent, all learners considered the content was delivered in a very good or excellent way. High percentages of learners stated to be satisfied with the distribution of the content among each module (94%) and 86% of learners stated that your level of dedication was high or very high. Concerning improvement of knowledge, we found an evolution of 4 percentual points between pre and post-tests (p = 0,001). The learners have identified two main changes on their practice, the first one related with patient care, improving communications and positioning skills and the second one related with image interpretation, improving the image processing and analyses. Conclusion: These global results show that e-learning can provide statistically relevant knowledge gains in Radiographers. This course is an important contribution to the improvement of mammography education, impacting on the development of students' and radiographers' skills.

Abstract
Missing data is an issue often addressed with imputation strategies that replace the missing values with plausible ones. A trend in these strategies is the use of generative models, one being Variational Autoencoders. However, the default loss function of this method gives the same importance to all data, while a more suitable solution should focus on the missing values. In this work an extension of this method with a custom loss function is introduced (Variational Autoencoder with Weighted Loss). The method was compared with state-of-the-art generative models and the results showed improvements higher than 40% in several settings. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.