Construction and Utilization of a Neural Network Model to Predict Current Procedural Terminology Codes from Pathology Report Texts

• Published in: Journal of pathology informatics Vol. 10; no. 1; p. 13
• Main Author: Ye, Jay J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2019; Medknow Publications & Media Pvt. Ltd; Wolters Kluwer - Medknow; Elsevier
• Subjects: Accuracy; Appendectomy; Applications programs; Codes; Coding; Current procedural terminology codes; Datasets; deep learning; Error correction & detection; Error detection; Information systems; neural network; Neural networks; Original; Outpatient care facilities; Pathology; Predictions; Programming languages; Terminology; Texts; Training; Palo Alto California; United States > US; deep learning; Current procedural terminology codes; neural network
• DOI: 10.4103/jpi.jpi_3_19
• ISSN: 2153-3539; 2229-5089

Background: At our department, each specimen was assigned a tentative current procedural terminology (CPT) code at accessioning. The codes were subject to subsequent changes by pathologist assistants and pathologists. After the cases had been finalized, their CPT codes went through a final verification step by coding staff, with the aid of a keyword-based CPT code-checking web application. Greater than 97% of the initial assignments were correct. This article describes the construction of a CPT code-predicting neural network model and its incorporation into the CPT code-checking application. Materials and Methods: R programming language was used. Pathology report texts and CPT codes for the cases finalized during January 1-November 30, 2018, were retrieved from the database. The order of the specimens was randomized before the data were partitioned into training and validation set. R Keras package was used for both model training and prediction. The chosen neural network had a three-layer architecture consisting of a word-embedding layer, a bidirectional long short-term memory (LSTM) layer, and a densely connected layer. It used concatenated header-diagnosis texts as the input. Results: The model predicted CPT codes in both the validation data set and the test data set with an accuracy of 97.5% and 97.6%, respectively. Closer examination of the test data set (cases from December 1 to 27, 2018) revealed two interesting observations. First, among the specimens that had incorrect initial CPT code assignments, the model disagreed with the initial assignments in 73.6% (117/159) and agreed in 26.4% (42/159). Second, the model identified nine additional specimens with incorrect CPT codes that had evaded all steps of checking. Conclusions: A neural network model using report texts to predict CPT codes can achieve high accuracy in prediction and moderate sensitivity in error detection. Neural networks may play increasing roles in CPT coding in surgical pathology.