Open Access

Sleep deprivation is a significant contributor to various diseases, leading to poor cognitive function, decreased performance, and heart disorders. Insomnia, the most prevalent sleep disorder, requires more effective diagnosis and screening for proper treatment. Actigraphic data and its combination with physiological sensors like electroencephalogram (EEG), electrocardiogram (ECG), and body temperature have proven significant in predicting insomnia using machine learning methods. Studies focusing solely on actigraphic data achieved an accuracy of 84%, combining it with other wearable devices increased accuracy to 88%, and 2-channel EEG alone yielded an accuracy of 92%, but limits scalability and practicality in real-world settings. Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. The top-ranked features are predominantly sleep-related and time-domain RR interval. The dependent variables in our study have been extracted from the self-report Pittsburgh Sleep Quality Index questionnaire responses. Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.

Energy consumption aspects of machine learning classifiers are important for research and practice as well. Due to sparse research in this area, a prototype of a recommender system was developed to provide energy consumption recommendations of different possible classifiers. The prototype is demonstrated as well as discussed and future research points are derived.

Recent standardization work for database languages has reflected the growing use of typed graph models (TGM) in application development. Such data models are frequently only used early in the design process, and not reflected directly in underlying physical database. In previous work, we have added support to a relational database management system (RDBMS) with role-based structures to ensure that relevant data models are not separately declared in each application but are an important part of the database implementation. In this work, we implement this approach for the TGM: the resulting database implementation is novel in retaining the best features of the graph-based and relational database technologies.

General practice-based research networks have become an integral tool to gain medical knowledge from primary care in many countries. For this purpose, a scalable IT-infrastructure is presented considering the limiting peculiarities in the German health system and enabling GPs to participate in clinical studies based on their patient population. The infrastructure consists of a central study management server and local clients for each practice. It adopts to the currently limited digital connectivity of GP practices, data protection regulations for clinical data and the needs of the medical staff to manage a clinical study. The infrastructure is in production at the four university hospitals in the state of Baden-Württemberg. Until now three clinical studies with over 70 GPs and 350 Participants are successfully conducted or have been finished. Further clinical studies are in the planning stages.

This paper reviews the changes for database technology represented by the current development of the draft international standard ISO 39075 (Database Languages - GQL), which seeks a unified specification for property graphs and knowledge graphs. This paper examines these current developments as part of our review of the evolution of database technology, and their relation to the longer-term goal of supporting the Semantic Web using relational technology.