Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study.

Aging affects the 12-lead electrocardiogram (ECG) and correlates with cardiovascular disease (CVD). AI-ECG models estimate aging effects as a novel biomarker but have only been evaluated on single ECGs-without utilizing longitudinal data. We validated an AI-ECG model, originally trained on Brazilian data, using a German cohort with over 20 years of follow-up, demonstrating similar performance (r = 0.

Benchmarking the Impact of Noise on Deep Learning-Based Classification of Atrial Fibrillation in 12-Lead ECG.

Electrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise efficiently, but its influence on the accuracy of these methods is still unclear. Therefore, we benchmark the influence of four types of noise on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead electrocardiograms.

Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria.

Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack of explainability. In this work, we apply explainable attribution methods to a pre-trained deep neural network for abnormality classification in 12-lead electrocardiography to open this "black box" and understand the relationship between model prediction and learned features. We classify data from two public databases (CPSC 2018, PTB-XL) and the attribution methods assign a "relevance score" to each sample of the classified signals.

Improving Findability of Digital Assets in Research Data Repositories Using the W3C DCAT Vocabulary.

Research data management requires stable, trustworthy repositories to safeguard scientific research results. In this context, rich markup with metadata is crucial for the discoverability and interpretability of the relevant resources. SEEK is a web-based software to manage all important artifacts of a research project, including project structures, involved actors, documents and datasets.

Application of Pre-Trained Deep Learning Models for Clinical ECGs.

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians.

Facilitating Study and Item Level Browsing for Clinical and Epidemiological COVID-19 Studies.

COVID-19 poses a major challenge to individuals and societies around the world. Yet, it is difficult to obtain a good overview of studies across different medical fields of research such as clinical trials, epidemiology, and public health. Here, we describe a consensus metadata model to facilitate structured searches of COVID-19 studies and resources along with its implementation in three linked complementary web-based platforms.

Applying FAIRness: Redesigning a Biomedical Informatics Research Data Management Pipeline.

Background:  Managing research data in biomedical informatics research requires solid data governance rules to guarantee sustainable operation, as it generally involves several professions and multiple sites. As every discipline involved in biomedical research applies its own set of tools and methods, research data as well as applied methods tend to branch out into numerous intermediate and output data objects, making it very difficult to reproduce research results.

Objectives:  This article gives an overview of our implementation status applying the Findability, Accessibility, Interoperability and Reusability (FAIR) Guiding Principles for scientific data management and stewardship onto our research data management pipeline focusing on the software tools that are in use.

Provenance Solutions for Medical Research in Heterogeneous IT-Infrastructure: An Implementation Roadmap.

Research data generated in large projects raise challenges about not only data analytics but also data quality assessments and data governance. The provenance of a data set - that is the history of data sets - holds information relevant to technicians and non-technicians and is able to answer questions regarding data quality, transparency, and more. We propose an implementation roadmap to extract, store, and utilize provenance records in order to make provenance available to data analysts, research subjects, privacy officers, and machines (machine readability).

Enabling Pedigree Visualization and Analysis in tranSMART.

This paper examines the relevance of genetic pedigree data in the context of medical research platforms. By surveying currently available tools for visualization and analysis of this data type and by considering possible use cases that could make usage of the combination of singular patient data and pedigree data, the advantages of integrating the data type into a medical research platform were shown. In a practical work step, an integration procedure of pedigree data into tranSMART was created.