Neurosurgical literature classification – Evaluation of three automated methods and time trend analysis of the literature

TL;DR

This paper introduces a new method for classifying neurosurgical literature into subspecialties and compares its performance with existing methods and human experts.

Contribution

A novel similarity-based text classification method is introduced and evaluated for neurosurgical literature classification.

Findings

The introduced similarity-based method showed the highest agreement with human experts in classifying neurosurgical literature.

Oncology has been the most prominent subspecialty in neurosurgical publications over time.

The method's performance is promising in scenarios with limited preclassified data.

Abstract

Automated supervised text classification methods require preclassified training data. Their application in scenarios that a large amount of preclassified data is not accessible is challenging. Neurosurgical literature classification into subspecialties is an example of this situation. We have introduced an automated similarity-based text classification method, evaluated it along with two other automated methods and applied the introduced method in neurosurgical literature classification.

Performance of an introduced similarity-based text classification method along with two other automated methods (Lbl2Vec and keyword counting-based methods) was compared with performance of two senior neurosurgery registrars in classification of neurosurgical literature to 5 subspecialties. The Kappa-statistic measure of interrater agreement, overall marginal homogeneity using the Stuart-Maxwell test, marginal homogeneity relative to individual categories using McNemar tests and the sensitivity and specificity of each of the three methods were calculated.

The introduced method was used to classify 211617 neurosurgical publications indexed in Pubmed to different subspecialties based on keywords extracted from subspecialty sections of a neurosurgery textbook.

The introduced similarity-based method showed the highest agreement with the registrars (raw agreement and Kappa value) followed by the Lbl2Vec and the counting-based method. Classifications of the English neurosurgical publications indexed in Pubmed into categories of Oncology, Vascular, Spine and functional using the introduced similarity-based method were more reliable (closer to the registrars’ classifications) than Cranial trauma. The classifications and future forecast showed highest publications in Oncology, followed by Cranial trauma, Vascular, spine and functional neurosurgery.

The classification of the English neurosurgical publications indexed in Pubmed to different subspecialties, using the introduced method, shows that Oncology and tumour has been the main battleground for the neurosurgeons over years and probably in the near future. The performance of the introduced classification method in comparison with the human performance shows its potential application in the situations that enough preclassified data are not accessible for automated text classification.

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