Comparison of deep learning approaches to estimate injury severity from the International Classification of Diseases codes

The injury severity classification based on the Abbreviated Injury Scale (AIS) provides information that allows for standardized comparisons for injury research. However, the majority of injury data is captured using the International Classification of Diseases (ICD), which lacks injury severity information. It has been shown that the encoder-decoder-based neural machine translation (NMT) model is more accurate than other methods for determining injury severity from ICD codes. The objectives of this project were to determine if feed-forward neural networks (FFNN) perform as well as NMT and to determine if direct estimation of injury severity is more accurate than using AIS codes as an intermediary (indirect method).