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Semiautomated Approach for Muscle Weakness Detection in Clinical Texts
Gleb Danilov, Michael Shifrin, Yuliya Strunina, Konstantin Kotik, Tatyana Tsukanova, Tatiana Pronkina, Timur Ishankulov, Elizaveta Makashova, Alexandra Kosyrkova, Semen Melchenko, Timur Zagidullin, Alexander Potapov
55 - 58
10.3233/SHTI200492
Research Article
Studies in Health Technology and Informatics
Volume 272: The Importance of Health Informatics in Public Health during a Pandemic
Abstract

The automated detection of adverse events in medical records might be a cost-effective solution for patient safety management or pharmacovigilance. Our group proposed an information extraction algorithm (IEA) for detecting adverse events in neurosurgery using documents written in a natural rich-in-morphology language. In this paper, we challenge to optimize and evaluate its performance for the detection of any extremity muscle weakness in clinical texts. Our algorithm shows the accuracy of 0.96 and ROC AUC = 0.96 and might be easily implemented in other medical domains.

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