Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery - Biswas, Sayan; McMenemy, Lareyna; Sarkar, Ved; MacArthur, Joshua; Snowdon, Ella; Tetlow, Callum; George, K. Joshi (Frontiers in Surgery, 2023)
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.A single centre retrospective case series analysis from 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. An extreme gradient-boosting NLP algorithm was developed and assessed on 5 performance metrics: accuracy, area under receiver-operating curve (AUC), positive predictive value (PPV), specificity, and Brier score.942 patients were used in the training set and 235 patients in the testing set. The average age of the cohort as 53.900 ± 16.153 years, with a female pre-dominance of 616 patients (52.3%). The models achieved an aggregate accuracy of >91%, a specificity of >91%, a PPV of >84%, an area under the curve of >0.933 and a brier score loss of ≤0.082. Decision curve analysis also revealed that these NLP algorithms possessed great clinical net benefit at all possible threshold probabilities. Global and local model interpretation analysis further highlighted relevant clinically useful features (words) important in classifying the presence of each entity respectively.These NLP algorithms can help monitor surgical performance and complications in an automated fashion by identifying and classifying the presence of various intra-operative elements in lumbar spine surgery.