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Development of Machine Learning and Natural Language Processing Algorithms for Preoperative Prediction and Automated Identification of Intraoperative Vascular Injury in Anterior Lumbar Spine Surgery by Aditya V. Karhade; Michiel E.R. Bongers; Olivier Q. Groot; Thomas D. Cha; Terence P. Doorly; Harold A. Fogel; Stuart H. Hershman; Daniel G. Tobert; Sunita D. Srivastava; Christopher M. Bono; James D. Kang; Mitchel B. Harris; Joseph H. Schwab is a Medicine article available to read on EtoBox.

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## Background: Intraoperative vascular injury (vi) may be an unavoidable complication of anterior lumbar spine surgery; however, vascular injury has implications for quality and safety reporting as this intraoperative complication may result in serious bleeding, thrombosis, and postoperative stricture. ## Purpose: The purpose of this study was to (1) develop machine learning algorithms for preoperative prediction of vi and (2) develop natural language processing (nlp) algorithms for automated surveillance of intraoperative vi from free-text operative notes. ## Patient sample: Adult patients, 18 years or age or older, undergoing anterior lumbar spine surgery at two academic and three community medical centers were included in this analysis. ## Outcome measures: The primary outcome was unintended vi during anterior lumbar spine surgery. ## Methods: Manual review of free-text operative notes was used to identify patients who had unintended vi. the available population was split into training and testing cohorts. five machine learning algorithms were developed for preoperative prediction of vi. an nlp algorithm was trained for automated detection of intraoperative vi from free-text ope

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Author

Aditya V. Karhade; Michiel E.R. Bongers; Olivier Q. Groot; Thomas D. Cha; Terence P. Doorly; Harold A. Fogel; Stuart H. Hershman; Daniel G. Tobert; Sunita D. Srivastava; Christopher M. Bono; James D. Kang; Mitchel B. Harris; Joseph H. Schwab

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 1529-9430)

Published

2020

Language

EN

Field

Medicine (Health Sciences)