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
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.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com