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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.

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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.

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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.

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Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection.