Abstract
The Abbreviated Injury Scale (AIS) is an essential tool for injury research since it allows for comparisons of injury severity among patients, however, the International Classification of Diseases (ICD) is more widely used to capture medical information. The problem of conversion between these two medical coding systems has similarities to the challenges encountered in language translation. We therefore hypothesize that neural machine translation (NMT), a deep learning technique which is commonly used for human language translation, could be used to convert ICD codes to AIS. The objective of this study was to compare the accuracy of a NMT model for determining injury severity compared to two established methods of conversion. The injury severity classifications used for this study were Injury Severity Score (ISS) ≥ 16, Maximum AIS severity (MAIS) ≥ 3, and MAIS ≥ 2. Data from a US national trauma registry, which has patient injuries coded in both AIS and ICD, was used to train a NMT model. Testing data from a separate year was used to determine the accuracy of the NMT model predictions against the actual ISS recorded in the registry. The prediction accuracy of the NMT model was compared to that of the official Association for the Advancement of Automotive Medicine (AAAM) ICD-AIS map and the R package 'ICD Program for Injury Categorization in R' (ICDPIC-R). The results show that the NMT model was the most accurate across all injury severity classifications, followed by the ICD-AIS map and then ICDPIC-R package. The NMT model also showed the highest correlation between the predicted and observe ISS scores. Overall, NMT appears to be a promising method for predicting injury severity from ICD codes, however, validation in external databases is needed.