Automatic ICD-10 coding : Natural language processing for German MRI reports

Abstract

The medical coding of radiology reports is essential for a good quality of care and correct billing, but at the same time acomplex and error-prone task. To assess the performance of natural language processing (NLP) for ICD-10 coding of German radiology reports using fine tuning of suitable language models. This retrospective study included all magnetic resonance imaging (MRI) radiology reports acquired at our institution between 2010 and 2020. The codes on discharge ICD-10 were matched to the corresponding reports to construct adataset for multiclass classification. Fine tuning of GermanBERT and flanT5 was carried out on the total dataset (dstotal) containing 1035 different ICD-10codes and 2 reduced subsets containing the 100 (ds100) and 50 (ds50) most frequent codes. The performance of the model was assessed using top‑k accuracy for k = 1, 3 and 5. In an ablation study both models were trained on the accompanying metadata and the radiology report alone. The total dataset consisted of 100,672 radiology reports, the reduced subsets ds100 of 68,103 and ds50 of 52,293 reports. The performance of the model increased when several of the best predictions of the model were taken into consideration, when the number of target classes was reduced and the metadata were combined with the report. The flanT5 outperformed GermanBERT across all datasets and metrics and was is suited as a medical coding assistant, achieving atop 3 accuracy of nearly 70% in the real-world dataset dstotal. Finely tuned language models can reliably predict ICD-10codes of German magnetic resonance imaging (MRI) radiology reports across various settings. As acoding assistant flanT5 can guide medical coders to make informed decisions and potentially reduce the workload.