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By and Ayoub Bagheri

Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but manual classification is an enormously time-consuming endeavour. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but limited performance of machine learning models, the necessity of gigantic datasets and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in free medical text in cardiology. We focussed on frequently used and well defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21) or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5,548 discharge letters and validated in 5,089 discharge and procedural letters. As in clinical practice discharge letters may be labelled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy sensitive information included in discharge letters, we added a de-identification step. Performance was high, with F1 scores of 0.76 – 0.99 for three-character and 0.87-0.98 for four-character ICD-10 codes and was best by using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease administrative burden of classifying discharge diagnoses and may serve a scaffold for reimbursement and research applications.