Affiliation:
1. University of California, San Diego
2. University of California San Diego Health
Abstract
Abstract
Purpose: A major source of inefficiency in the operating room is the mismatch between scheduled versus actual surgical time. The purpose of this study was to demonstrate a proof-of-concept study for predicting case duration by applying natural language processing (NLP) and machine learning that interpret radiology reports for patients undergoing radius fracture repair.
Methods: Logistic regression, random forest, and artificial neural networks (ANN) were tested without NLP and with bag-of-words. Another NLP method tested used ANN and Bidirectional Encoder Representations from Transformers specifically pre-trained on clinical notes (ClinicalBERT). A total of 201 cases were included. The data were split into 70% training and 30% test sets. The average root mean squared error (RMSE) (and 95% confidence interval [CI]) from 10-fold cross-validation on the training set were used to develop each model. Models were then compared to a baseline model, which used historic averages to predict surgical time.
Results: The average RMSE was lowest using ANN with ClinicalBERT (25.6 minutes, 95% CI: 21.5 - 29.7), which was significantly (P<0.001) lower than the baseline model (39.3 minutes, 95% CI: 30.9 - 47.7). Using the ANN and ClinicalBERT on the test set, the percentage of accurately predicted cases, which was defined by the actual surgical duration within 15% of the predicted surgical duration, increased from 26.8% to 58.9% (P<0.001).
Conclusion: This proof-of-concept study demonstrated the successful application of NLP and machine leaning to extract features from unstructured clinical data resulting in improved prediction accuracy for surgical case duration.
Publisher
Research Square Platform LLC
Cited by
1 articles.
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