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.
Keywords: Adverse Events; Annotation; Natural Language Processing; Neurosurgery.