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Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clinical assessment instruments include the Delirium Observation Screening Scale (DOSS) or the Confusion Assessment Method (CAM). The aim of this work is to analyze the performance of machine learning approaches to detect delirium based on DOSS and CAM information obtained from two geropsychiatric wards in Tyrol. From a machine learning perspective, the questions of these two assessment instruments represent the features and the ICD 10 diagnoses of delirium (yes/no) is the corresponding class variable. We compare seven popular classification methods and analyze the performance and interpretability of the learning models. As our dataset is highly imbalanced, we also evaluate the effect of common sampling methods including down- and up-sampling methods as well as hybrid methods. Our results indicate a high predictive ability of advanced methods such as Random Forest that can handle even unbalanced datasets. Overall, combining a good performance of a prediction model with the ability of users to understand the prediction is challenging. However, for clinical application in fully electronic settings, a good performance seems to be more important than an easy interpretation of the prediction by the user. On the other hand, explanations of decisions are often needed to assess other criteria such as safety. [ABSTRACT FROM AUTHOR]

Socio-constructive instructional designs for online-based learning focus on interaction and communication of students to allow in-depth learning. The objective of this study is to analyze whether increased interaction of students in online-based learning settings may contribute to better outcome. We developed indicators for presence, participation, and interactivity of students. We extracted log data from the learning management system for 31 students in 10 online courses (n=123 course attendances). We correlated indicators to final grades and also applied a decision tree based machine learning approach. We found only weak to moderate correlations between the indicators and final grades, but acceptable results concerning prediction of students' success based on the indicators. Our results support the theory that student presence and participation in online-based courses is related to learning outcome. [ABSTRACT FROM AUTHOR]

Background: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare. Objectives: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity. Methods: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry. Results: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation. Conclusion: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups. [ABSTRACT FROM AUTHOR]

Background: The Community of Inquiry (CoI) describes success factors for online-based learning. Objectives: To develop approaches for automatic analysis of CoI to be visualized within student and teacher dashboards. Methods: Extending indicators from social network analysis and linguistics; evaluation within a case study. Results: The project is just starting. Conclusion: Results will help to better understand and improve cooperative online-based learning in higher education. [ABSTRACT FROM AUTHOR]

Background: Process mining is a promising field of data analytics that is yet to be applied broadly in healthcare. It can streamline the care process, leading to a higher quality of care, increased patient safety and lower costs., Objectives: To get deeper insights into the emergence and detectability of delirium in a gerontopsychiatric setting., Methods: We use process mining to create process models from routinely collected, anonymised nursing data from two gerontopsychiatric wards. We analyse these models to get a longitudinal view of the care processes., Results: The process models comprise all activities during patients' stays but are too extensive and challenging to interpret due to the wide variation in care paths. Although the models give insight into frequent paths and activities, they are insufficient to explain the emergence of delirium meaningfully. No apparent difference between stays with or without delirium could be detected., Conclusion: Conducting process mining on routinely collected data is easy, but the interpretation of the results was a challenge. We identified four limitations associated with using this data and gave recommendations on adapting it for further analysis.

Socio-constructive instructional designs for online-based learning focus on interaction and communication of students to allow in-depth learning. The objective of this study is to analyze whether increased interaction of students in online-based learning settings may contribute to better outcome. We developed indicators for presence, participation, and interactivity of students. We extracted log data from the learning management system for 31 students in 10 online courses (n=123 course attendances). We correlated indicators to final grades and also applied a decision tree based machine learning approach. We found only weak to moderate correlations between the indicators and final grades, but acceptable results concerning prediction of students' success based on the indicators. Our results support the theory that student presence and participation in online-based courses is related to learning outcome.