Sessions

Electrodermal activity (EDA) reflects sympathetic nervous system activitythrough sweating-related changes in skin conductance. Decomposition analysisis used to deconvolve the EDA into slow and fast varying tonic and phasicactivity, respectively. In this study, we used machine learning models to comparethe performance of two EDA decomposition algorithms to detect emotions such asamusing, boring, relaxing, and scary. The EDA data considered in this study wereobtained from the publicly available Continuously Annotated Signals of Emotion(CASE) dataset. Initially, we pre-processed and deconvolved the EDA data intotonic and phasic components using decomposition methods such as cvxEDA andBayesianEDA. Further, twelve-time domain features were extracted from the phasiccomponent of EDA data. Finally, we applied machine learning techniques suchas logistic regression (LR) and support vector machine (SVM), to evaluate the performanceof the decomposition method. Our results imply that the BayesianEDAdecomposition method outperforms the cvxEDA. The mean of first derivative featurediscriminated all the considered emotional pairs with high statistical significance(p<0.05). SVM was able to detect emotions better than the LR classifier.We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision,and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively,using BayesianEDA and SVM classifier. The proposed framework can be utilizedto detect emotional states for the early diagnosis of psychological conditions.

Predicting Progression of Type 2 Diabetes using Primary Care Data with the Help of Machine Learning

Sweden is a leader in digital health, with a legacy in Life Science and Tech. The Swedish ecosystem has been accelerated by advanced digital infrastructure, high quality patient data, and cutting-edge support and diagnostic technologies. With a drive to continually improve healthcare with data-driven and AI solutions, Sweden ranks among the best performing countries for connected innovation, and the global demand for Swedish digital health solutions is high

As a free service you are invited to participate in a matchmaking event open to all delegates, presenters and exhibition participants. The aim of the event is to create win-win- situations and perhaps future collaborations between researchers, practitioners, patients and industry.The matchmaking is only available for conference delegates, investors and exhibitors.

An introduction to openEHR for beginners. This is part 2, covering Clinical modeling, Technical implementation, Querying, Tools and examples in more detail.

The workshop aims at improving the completeness and interoperability of data dictionaries through harmonization and standardization. First, the focus lies on the syntactic and semantic level in order to derive concrete technical specifications and implementations: Based on a comprehensive review of existing standards and data dictionaries in common systems, frequently used attributes were identified. In a second step, a "Minimal Information Model" is discussed, in which typical description fields are listed, terminologically cleaned and arranged in a modular system to enable easy use and user-specific expansion. Crosswalks to existing vocabularies and conventions or to international standards are to be discussed.Practical mapping of your Data Dicitonary is possible as a practical exercise!