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Collaborative learning incorporates a social component in distance education to minimize the disadvantages of studying in solitude. Frequent analysis of student interactions is required for assessing collaboration. Collaboration analytics arose as a discipline to study student interactions and to promote active participation in e-learning environments. Unfortunately, researchers have been more focused on finding methods to solve collaboration problems than on explaining the results to tutors and students. Yet if students do not understand the results of collaboration analysis methods, they will rarely follow their advice. In this paper we propose a tool that analyzes student interactions and visually explains the collaboration circumstances to provoke the self-reflection and promote the sensemaking about collaboration. The tool presents a visual explanatory decision tree that graphically highlights student collaboration circumstances and helps to understand the reasoning followed by the tool when prescribing a recommendation. An assessment of the tool has demonstrated: (1) the students collaboration circumstances showed by the tool are easy to understand and (2) the students could realize the possible actions to improve the collaboration process. [ABSTRACT FROM AUTHOR]

Highlights: [•] Proposed a recommender system in a collaborative learning environment. [•] Created an influence diagram using expert knowledge and student interactions data. [•] The influence diagram offers a recommendation decision table. [•] Machine learning methods analyze the decisions and offer a recommendation tree. [•] The expert identifies four different scenarios in the recommendation tree. [ABSTRACT FROM AUTHOR]

Introduction: Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEAs for very small problems. Influence diagrams can model much larger problems, but only when the decisions are totally ordered.Objective: To develop a CEA method for problems with unordered or partially ordered decisions, such as finding the optimal sequence of tests for diagnosing a disease.Methods: We explain how to model those problems using decision analysis networks (DANs), a new type of probabilistic graphical model, somewhat similar to Bayesian networks and influence diagrams. We present an algorithm for evaluating DANs with two criteria, cost and effectiveness, and perform some experiments to study its computational efficiency. We illustrate the representation framework and the algorithm using a hypothetical example involving two therapies and several tests and then present a DAN for a real-world problem, the mediastinal staging of non-small cell lung cancer.Results: The evaluation of a DAN with two criteria, cost and effectiveness, returns a set of intervals for the willingness to pay, separated by incremental cost-effectiveness ratios (ICERs). The cost, the effectiveness, and the optimal intervention are specific for each interval, i.e., they depend on the willingness to pay.Conclusion: Problems involving several unordered decisions can be modeled with DANs and evaluated in a reasonable amount of time. OpenMarkov, an open-source software tool developed by our research group, can be used to build the models and evaluate them using a graphical user interface. [ABSTRACT FROM AUTHOR]

• A novel multi-input convolutional neural network is proposed to detect breast cancer. • The model combines thermal images of different views with personal and clinical data. • The model achieves better results than the ones only considering the front views. • Personal and clinical data helps to better detect patients with cancer. [Display omitted] Breast cancer is the most common cancer in women. While mammography is the most widely used screening technique for the early detection of this disease, it has several disadvantages such as radiation exposure or high economic cost. Recently, multiple authors studied the ability of machine learning algorithms for early diagnosis of breast cancer using thermal images, showing that thermography can be considered as a complementary test to mammography, or even as a primary test under certain circumstances. Moreover, although some personal and clinical data are considered risk factors of breast cancer, none of these works considered that information jointly with thermal images. We propose a novel approach for early detection of breast cancer combining thermal images of different views with personal and clinical data, building a multi-input classification model which exploits the benefits of convolutional neural networks for image analysis. First, we searched for structures using only thermal images. Next, we added the clinical data as a new branch of each of these structures, aiming to improve its performance. We applied our method to the most widely used public database of breast thermal images, the Database for Mastology Research with Infrared Image. The best model achieves a 97% accuracy and an area under the ROC curve of 0.99, with a specificity of 100% and a sensitivity of 83%. After studying the impact of thermal images and personal and clinical data on multi-input convolutional neural networks for breast cancer diagnosis, we conclude that: (1) adding the lateral views to the front view improves the performance of the classification model, and (2) including personal and clinical data helps the model to recognize sick patients. [ABSTRACT FROM AUTHOR]