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During recent years, maintaining scientific and medical databases has turned out to be a major challenge in the medical sector. The medical data regarding the patients comprise diverse diagnostic medical reports and features for such disease need to be recorded carefully for providing high-quality treatments. Therefore, this paper proposes a hybrid deep convolutional neural network (HDCNN)-based COOT approach to predict the risk of diseases. Initially, an improved crossover-based levy flight optimization algorithm (ICLFDO) algorithm is employed in selecting and processing the unstructured textual data. Then later, the HDCNN-COOT approach predicts the diseases more accurately. In addition to this, the classifier predicts whether the patient is at risk of disease in the future or not. The efficiency of the proposed model is evaluated by using a dataset obtained from University Hospital of Ludwig Maximilian University of Munich, Germany. The data collected in the data center include 29,477,035 data items from 36,082 patients. The proposed model is capable of using structured data and uses textual data of the patients. Finally, the higher classification accuracy and improved performance of the classifiers can be viewed using the experimental results conducted on five different datasets. [ABSTRACT FROM AUTHOR]

Background This paper explores the role of providing structure for students within a new blended-learning family medicine curriculum at Saarland University. This study intends to clarify the role of different structural elements for different learning approaches (deep vs. structured vs. surface learning). Methods This study is based on a mixed-methods approach. Fifth year medical students' (n = 84) learning approaches, measured by a shortened version of the ASSIST questionnaire, were correlated with students' academic performance in state exams and the family medicine exam. Based on learning approaches and age, participants were purposefully sampled into two semi-structured group interviews with seven participants each. Results Although surface approach learning weakly correlated with lower performance in the first state exam (rho = 0.231), surface approach learning did not correlate with lower performance in the family medicine assessment. Based on qualitative results, the structural design of the curriculum seemed to have a high impact on students' satisfaction and motivation to learn. The reduction of extraneous cognitive load was identified as effective in motivating students to study. Deep-learning students highlighted the positive effects of curriculum activities that foster germane cognitive load through schema- based learning. Conclusions Especially for surface approach learners it may be important to provide structured guidance and provide a clear alignment of course content in a blended-learning curriculum, even in higher education. Deep and structured approach learners also benefit from a clear course structure that keeps extraneous cognitive load low and fosters schema acquisition. [ABSTRACT FROM AUTHOR]