Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Studies in health technology and informatics ; h5-index 23.0

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.

Danilov Gleb, Shifrin Michael, Strunina Yuliya, Kotik Konstantin, Tsukanova Tatyana, Pronkina Tatiana, Ishankulov Timur, Makashova Elizaveta, Kosyrkova Alexandra, Melchenko Semen, Zagidullin Timur, Potapov Alexander

2020-Jun-26

Adverse Events, Annotation, Natural Language Processing, Neurosurgery