Artificial intelligence for prediction of International Classification of Disease codes

Abstract

Background: The automatic coding of electronic medical records with ICD (International Classification of Diseases) codes is an area of interest due to its potential in improving efficiency and streamlining processes such as billing and outcome tracking. Artificial intelligence (AI), particularly convolutional neural networks (CNN) have been suggested as a possible mechanism for automatic coding. To this end, a rapid review has been undertaken in order to assess the current use of CNN in predicting ICD codes from electronic medical records. Methods: After screening PubMed, IEEE Xplore, Scopus, and Google Scholar, 11 studies were analyzed for the use of CNN in predicting ICD codes. We used artificial intelligence and ICD prediction as keywords in the search strategy. Results: The analysis yielded a recommendation to further explore and research CNN frameworks as a promising lead to automatic ICD coding when paired with word embedding and/or neural transfer learning, while keeping research open to a wide variety of AI techniques. Conclusion: CNN frameworks are promising for the prediction of ICD codes from clinical notes.