No abstract

The real-time alerting of every worsening RIFLE class by the acute kidney injury sniffer increased the number and timeliness of early therapeutic interventions. The borderline significant improvement of short-term renal outcome in the RIFLE class risk patients needs to be confirmed in a large multicenter trial.

Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects’ demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine.

The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics, or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the sub-field of feature learning, i.e. deep learning, more specifically convolutional neural networks, is researched in this article. The objective of this article is to investigate if and how deep learning can be applied to infrared thermal video to automatically determine the condition of the machine. By applying this method on infrared thermal data in two use cases, i.e. machine fault detection and oil level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e. 95 % and 91.67 % accuracy for the respective use cases) without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained neural networks, important regions in the infrared thermal images can be identified related to specific conditions which can potentially lead to new physical insights.