Publications by authors named "E K Kutafina"
Article Synopsis
- * A consensus meeting in March 2024, attended by 28 experts and stakeholders, aimed to standardize research protocols for studying neuropathic pain using human peripheral tissues.
- * The meeting resulted in agreed-upon guidelines for phenotyping, laboratory protocols, statistical design, and data sharing to improve consistency in research and enhance understanding of neuropathic pain.
Article Synopsis
- Despite advancements in deep learning, its complex and opaque nature hinders widespread clinical adoption, prompting interest in concept-based interpretability, specifically using techniques like Testing with Concept Activation Vectors (TCAV).
- This study applies TCAV to abnormality detection in electroencephalography (EEG), utilizing the XceptionTime model on multi-channel physiological data to enhance interpretability and analyze concepts linked to EEG pathologies.
- The results indicate that TCAV scores align with clinical expectations, demonstrating its potential for improving interpretability in deep learning models and identifying biases in medical data.
Introduction: The Local Data Hub (LDH) is a platform for FAIR sharing of medical research (meta-)data. In order to promote the usage of LDH in different research communities, it is important to understand the domain-specific needs, solutions currently used for data organization and provide support for seamless uploads to a LDH. In this work, we analyze the use case of microneurography, which is an electrophysiological technique for analyzing neural activity.
View Article and Find Full Text PDFHow Data Infrastructure Deals with Bias Problems in Medical Imaging.
Stud Health Technol Inform
August 2024
The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative: the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles.
View Article and Find Full Text PDFHarmonizing Microneurography Metadata with Local Data Hubs: A Concept.
Stud Health Technol Inform
August 2024
This work aims to improve FAIR-ness of the microneurography research by integrating the local (meta)data to existing research data infrastructures. In the previous work, we developed an odML based solution for local metadata storage of microneurography data. However, this solution is limited to a narrow community.
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