An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language

Gleb Danilov; Michael Shifrin; Uliya Strunina; Tatyana Pronkina; Alexander Potapov

doi:10.3233/SHTI190051

An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language

Stud Health Technol Inform. 2019 Jul 4:262:194-197. doi: 10.3233/SHTI190051.

Authors

Gleb Danilov¹, Michael Shifrin¹, Uliya Strunina¹, Tatyana Pronkina¹, Alexander Potapov¹

Affiliation

¹ National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, RussianFederation.

PMID: 31349300
DOI: 10.3233/SHTI190051

Abstract

Rich-in-morphology language, such as Russian, present a challenge for extraction of professional medical information. In this paper, we report on our solution to identify adverse events (complications) in neurosurgery based on natural language processing and professional medical judgment. The algorithm we proposed is easily implemented and feasible in a broad spectrum of clinical studies.

Keywords: Adverse Events; Electronic Health Records; Natural Language Processing; Neurosurgery.

MeSH terms

Algorithms*
Data Mining
Electronic Health Records
Humans
Information Storage and Retrieval*
Natural Language Processing*
Neurosurgical Procedures* / adverse effects
Russia