Sessions

The automatic de-identification of clinical narrative text offers efficient patient data privacy protection and eases reuse of clinical data. CliniDeID applies an ensemble method combining deep and shallow machine learning with rule-based algorithms and is released as free and open-source solution to de-identify unstructured clinical text with high accuracy. It was recently evaluated with a selection of clinical text corpora and reached high sensitivity and positive predictive value.

We present a study comparing two mediums that can be used to communicate with allophone patients: a speech-enabled phraselator (BabelDr) and telephone interpreting. 

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.

May 21 – 24, 2023
Connect4Health via NordXDataspace at Vitalis
The Connect4Health healthcare connectathon using NordxDataspace aims to promote digital wallets and Solid pods as a data intermediary through a trusted data intermediation service to enable organisations to become part of the European Health Data Space while ensuring data privacy, human-centric use of personal data and digital rights. 

Separate registration required: https://www.mie2023.org/tutorials,With this tutorial, participants will gain a detailed insight into the Data Sharing Framework (DSF). The open source DSF enables users to execute biomedical research and healthcare delivery processes across organizations, and the tutorial will demonstrate this with examples from the German Medical Informatics Initiative (MII) funded by the Federal Ministry of Education and Research (BMBF). The tutorial will cover fundamental concepts of distributed processes, the DSFs architecture and key standards such as HL7 FHIR and BPMN 2.0. Participants will have the opportunity to gain hands-on experience with the DSF by working with different processes in a lab setting. Technical aspects such as authentication and authorization will be covered, as well as guidance on using the DSF for other use cases. This tutorial is designed for those involved in distributed research projects, including project members and software developers, as well as individuals interested in multi-organizational research projects.Additional information: https://dsf.dev/tutorials/MIE2023.html

This presentation concerns the results of pilot study conducted to provide directions for prescription of mHealth in Denmark.