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Introduction: Carbohydrate malabsorptions symptoms include intestinal fluid retention, causing diarrhea and abdominal distention. The aim of this work is to create a machine learning model that predicts carbohydrate malabsorption using H2 measurements from lactose and fructose tolerance tests. Methods: We compare the predictive ability of popular classifiers with classifiers that are specifically designed for time series data. Our approach was implemented using sklearn and sktime Python machine learning libraries. Results: The highest predictive ability for the fructose dataset was achieved using a Random Forest Classifier (balanced accuracy = 0.91). In contrast, the highest predictive ability (balanced accuracy = 0.81) for the lactose dataset was obtained using an IndividualTDE time classifier. Conclusion: Our results indicate a high predictive ability for distinguishing between carbohydrate malabsorptions. However, the detection of SIBO is challenging but adapted time classifier models could reach higher performances compared to standard methods. Our results could establish the basis of an expert system for diagnosing carbohydrate malabsorptions and SIBO, respectively.

Abstract Background Identifying group-specific characteristics in metabolic networks can provide better insight into evolutionary developments. Here, we present an approach to classify the three domains of life using topological information about the underlying metabolic networks. These networks have been shown to share domain-independent structural similarities, which pose a special challenge for our endeavour. We quantify specific structural information by using topological network descriptors to classify this set of metabolic networks. Such measures quantify the structural complexity of the underlying networks. In this study, we use such measures to capture domain-specific structural features of the metabolic networks to classify the data set. So far, it has been a challenging undertaking to examine what kind of structural complexity such measures do detect. In this paper, we apply two groups of topological network descriptors to metabolic networks and evaluate their classification performance. Moreover, we combine the two groups to perform a feature selection to estimate the structural features with the highest classification ability in order to optimize the classification performance. Results By combining the two groups, we can identify seven topological network descriptors that show a group-specific characteristic by ANOVA. A multivariate analysis using feature selection and supervised machine learning leads to a reasonable classification performance with a weighted F-score of 83.7% and an accuracy of 83.9%. We further demonstrate that our approach outperforms alternative methods. Also, our results reveal that entropy-based descriptors show the highest classification ability for this set of networks. Conclusions Our results show that these particular topological network descriptors are able to capture domain-specific structural characteristics for classifying metabolic networks between the three domains of life.

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]