![]() ![]() The decision curve analysis also revealed that these NLP algorithms possessed great clinical net benefit at all possible threshold probabilities. The models achieved an aggregate accuracy of >91%, a specificity of >91%, a PPV of >84%, an AUC of >0.933, and a Brier score loss of ≤0.082. The average age of the cohort was 53.900 ± 16.153 years, with a female predominance of 616 patients (52.3%). Results: A total of 942 patients were used in the training set and 235 patients, in the testing set. Extreme gradient-boosting NLP algorithms were developed and assessed on five performance metrics: accuracy, area under receiver-operating curve (AUC), positive predictive value (PPV), specificity, and Brier score. Methods: A single-centre retrospective case series analysis was conducted between January 2015 and June 2022, analysing operative notes of patients aged >18 years who underwent a primary lumbar discectomy and/or decompression at any lumbar level. 4Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal Hospital, Manchester, United Kingdomīackground: The aim of this study was to develop natural language processing (NLP) algorithms to conduct automated identification of incidental durotomy, wound drains, and the use of sutures or skin clips for wound closure, in free text operative notes of patients following lumbar surgery.3Division of Data Science, The Northern Care Alliance NHS Group, Manchester, United Kingdom.2College of Letters and Sciences, University of California, Berkeley, CA, United States.1Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.Sayan Biswas 1* † Lareyna McMenemy 1,† Ved Sarkar 2 Joshua MacArthur 1 Ella Snowdon 1 Callum Tetlow 3 K.
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