Abstract

Abstract
Traditional follow-up of patients with cardiovascular devices is still an activity that, in addition to serving an increasing population, requires a considerable amount of time and specialized human and technical resources. Our aim was to evaluate the applicability of the CareLink (R) (Medtronic, Minneapolis, MN) remote monitoring system as a complementary option to the follow-up of patients with implanted devices, between in-office visits. Evaluated outcomes included both clinical (event detection and time to diagnosis) and nonclinical (patient's satisfaction and economic costs) aspects. An observational, longitudinal, prospective study was conducted with patients from a Portuguese central hospital sampled by convenience during 1 week (43 patients). Data were collected in four moments: two in-office visits and two remote evaluations, reproducing 1 year of clinical follow-up. Data sources included health records, implant reports, initial demographic data collection, follow-up printouts, and a questionnaire. After selection criteria were verified, 15 patients (11 men [73%]) were included, 63.4 +/- 10.8 years old, re-presenting 14.0 +/- 6.3 implant months. Clinically, 15 events were detected (9 by remote monitoring and 6 by patient-initiated activation), of which only 9 were symptomatic. We verified that remote monitoring could detect both symptomatic and asymptomatic events, whereas patient-initiated activation only detected symptomatic ones (p = 0.028). Moreover, the mean diagnosis anticipation in patients with events was approximately 58 days (p < 0.001). In nonclinical terms, we observed high or very high satisfaction (67% and 33%, respectively) with using remote monitoring technology, but still 8 patients (53%) stated they preferred in-office visits. Finally, the introduction of remote monitoring technology has the ability to reduce total follow-up costs for patients by 25%. We conclude that the use of this system constitutes a viable complementary option to the follow-up of patients with implantable devices, between in-office visits.

Abstract
Introduction Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. Materials and methods This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Results Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. Conclusions The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

Abstract
Background: An asynchronous eLearning system was developed for radiographers in order to promote a better knowledge about senology and mammography. Objectives: to assess the learners' satisfaction. Methods: Target population included radiographers and radiography students, in order to assess eLearning satisfaction according to different experience levels in breast imaging. Satisfaction was measured through a questionnaire developed especially for eLearning systems, using a seven-point Likert scale. Main topics related are content, interface, personalization and learning community. Results: Overall, 85% of learners were satisfied with the course and 87,5% considered that the course is successful. Main areas that were evaluated by most learners in a positive way were interface and content (between six and seven-point); on the other hand, learning community presented a wider distribution of answers. Conclusions: The course provides an overall high degree of learner satisfaction, thus providing more effective knowledge gain on breast imaging for radiographers.

Abstract

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.