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Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from EHRs is heterogeneous and temporally irregular. Therefore, traditional machine learning methods like PCA are ill-suited for analysis of EHR-derived patient data. We propose to address these issues with a new methodology based on training a gated recurrent unit (GRU) autoencoder directly on health record data. Our method learns a low-dimensional feature space by training on patient data time series, where the time of each data point is expressed explicitly. We use positional encodings for time, allowing our model to better handle the temporal irregularity of the data. We apply our method to data from the Medical Information Mart for Intensive Care (MIMIC-III). Using our data-derived feature space, we can cluster patients into groups representing major classes of disease patterns. Additionally, we show that our feature space exhibits a rich substructure at multiple scales. [ABSTRACT FROM AUTHOR]

Modern intensive care medicine is caught between the conflicting demands of an efficient but also increasingly more technical intensive care treatment with numerous therapeutic options and, at the same time, an ageing society with increasing morbidity. This is reflected, among other things, in an increasing number of ventilated patients in intensive care units and an increasing proportion of patients for whom ventilation cannot easily be discontinued. Weaning from a ventilator, which can account for more than 50% of the total ventilation time, therefore plays a central role in this process. This main topic article presents the need for strategically wise and holistic actions to minimize the consequences of invasive mechanical ventilation for patients. An attempt is made to shed more light on individual aspects of the ventilation weaning process with high relevance for clinical practice. Especially for prolonged weaning from ventilation, many more concepts are needed than simply ending ventilation. [ABSTRACT FROM AUTHOR]

Excellent mechanical properties and corrosion resistance combined with low weight qualify β-titanium materials for lightweight applications in aviation, automotive and energy engineering. Thus far, actual applications of these materials have been limited due to high material costs and limited processing knowledge. One approach for developing resource-efficient manufacturing methods is the application of incremental forming methods. This article focuses on the development of the incremental spin extrusion process, which creates hollow profiles from solid bars. This method allows hollow shape manufacturing with a much higher flexibility than other forming methods and a significantly improved material utilization in comparison to machining methods, such as deep hole drilling. Beta-titanium alloys basically have very good cold forming suitability and the resulting material properties can be controlled. The application of incremental forming methods with high hydrostatic compressive stress is a promising manufacturing approach. The β-titanium Ti-10V-2Fe-3Al material has an excellent combination of the properties strength, ductility and fatigue strength. In order to utilize these properties the forming conditions and the temperature control need to be optimized. The investigations show that the Ti-10V-2Fe-3Al material can be formed only in a narrow semi-hot forming temperature window. The paper describes the investigation and presents results on the design of partial forming process sequences, forming properties, microstructure formation and failure prevention. The process design objective is a very fine microstructure with a homogeneous secondary α-phase and very small grained β-phase. [ABSTRACT FROM AUTHOR]

The Ertl algorithm for automated functional groups (FG) detection and extraction of organic molecules is implemented on the basis of the Chemistry Development Kit (CDK). A distinct impact of the chosen CDK aromaticity model is demonstrated by an FG analysis of the ChEMBL database compounds. The average performance of less than a millisecond for a single-molecule FG extraction allows for fast processing of even large compound databases. [ABSTRACT FROM AUTHOR]