28 results match your criteria: "National Medical Research Center for Neurosurgery named after N.N. Burdenko[Affiliation]"
Detection of Muscle Weakness in Medical Texts Using Natural Language Processing.
Stud Health Technol Inform
June 2020
Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
Article Synopsis
- Identifying adverse events in clinical documents is crucial for both retrospective research and monitoring treatment safety and cost-effectiveness.
- The study proposed semi-automated methods for detecting muscle weakness in preoperative clinical notes, aiming to improve predictions of paresis through images.
- The combined approach using semi-expert and machine learning methods achieved high sensitivity, specificity, and AUC scores, indicating its effectiveness for creating reliable training datasets for supervised machine learning.
An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language.
Stud Health Technol Inform
July 2019
National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, RussianFederation.
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.
View Article and Find Full Text PDFPrediction of Postoperative Hospital Stay with Deep Learning Based on 101 654 Operative Reports in Neurosurgery.
Stud Health Technol Inform
August 2019
National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
Electronic Health Records (EHRs) conceal a hidden knowledge that could be mined with data science tools. This is relevant for N.N.
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