Programpunkter

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

Patient generated health data (PGHD) integrates patients` values and preferences into decision-making. Digitalization enables the integration of PGHD with electronic patient records. However, the lack of digital tools and applications hampers the use of PGHD. The purpose of this workshop is to gather the current situation in research and to form a transdisciplinary network of researchers.Discussion topics:1.Identification of the researchers, research projects and digital tools and applications for PGHD to build a network of researchers in the field.2.Experiences and perceptions of the research and practice regarding the digital tools and applications for PGHD3.Recommendations for future research topics in the field of PGHD.Keywords. digitalization, shared decision making, patient generated health dataYou are warmly welcome!

Sharing our experience of mapping the Swiss Personalized Health Network Dataset to Fast Healthcare Interoperability Resources

Short introduction to the National Research Data Infrastructure for Personal Health Data (NFDI4Health) and proposed solutions towards findability and interoperability of clinical and epidemiological studies. Two services will be presented: the German Central Health Study Hub to support the findability of studies and the Metadata Annotation Workbench to support enrichment of data collection instruments with standardized vocabularies on the study item level.

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

Lets talk about interoperable order set creation than can be used by any EMR