Krivonosov, Mikhail; Nazarenko, Tatiana; Ushakov, Vadim; Vlasenko, Daniil; Zakharov, Denis; Chen, Shangbin; Blyus, Oleg; Krivonosov, Mikhail; Nazarenko, Tatiana; Ushakov, Vadim; Vlasenko, Daniil; Zakharov, Denis; Chen, Shangbin; Blyus, Oleg; Zaikin, Alexey; - view fewer (2024) Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks. Technologies , 13 (1) , Article 13. 10.3390/technologies13010013.

Preview

Text Zaikin_Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks_VoR.pdf Download (2MB) | Preview

Abstract

This paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good for addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional machine learning and Artificial Intelligence methods often struggle to find global optima due to the “curse of dimensionality”. To apply Geometric Deep Learning we propose a synolitic or ensemble graph representation of the data, a universal method that transforms any multidimensional dataset into a network, utilising only class labels from training data. The paper demonstrates the effectiveness of this approach through two classification tasks: synthetic and fMRI data from cognitive tasks. Convolutional Graph Neural Network architecture is then applied, and the results are compared with established machine learning algorithms. The findings highlight the robustness and interpretability of SGNNs in solving complex, high-dimensional classification problems.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item