Multinomial Classification of Neurosurgical Operations Using Gradient Boosting and Deep Learning Algorithms

Gleb Danilov; Konstantin Kotik; Michael Shifrin; Yulia Strunina; Tatiana Pronkina; Tatiana Tsukanova; Vladimir Nepomnyashiy; Nikolay Konovalov; Alexander Potapov

doi:10.3233/SHTI220754

Multinomial Classification of Neurosurgical Operations Using Gradient Boosting and Deep Learning Algorithms

Stud Health Technol Inform. 2022 Jun 29:295:418-421. doi: 10.3233/SHTI220754.

Authors

Gleb Danilov¹, Konstantin Kotik¹, Michael Shifrin¹, Yulia Strunina¹, Tatiana Pronkina¹, Tatiana Tsukanova¹, Vladimir Nepomnyashiy¹, Nikolay Konovalov¹, Alexander Potapov¹

Affiliation

¹ Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.

PMID: 35773900
DOI: 10.3233/SHTI220754

Abstract

This study aimed at testing the feasibility of neurosurgical procedures classification into 100+ classes using natural language processing and machine learning. A catboost algorithm and bidirectional recurrent neural network with a gated recurrent unit showed almost the same accuracy of ∼81%, with suggestions of correct class in top 2-3 scored classes up to 98.9%. The classification of neurosurgical procedures via machine learning appears to be a technically solvable task which can be additionally improved considering data enhancement and classes verification.

Keywords: Neurosurgery; artificial intelligence; classification; deep learning; machine learning; neurosurgical procedures.

MeSH terms

Algorithms
Deep Learning*
Machine Learning
Natural Language Processing
Neural Networks, Computer