Enhancing Machine Learning Models for Medical Coding: Diagnostic Codes Mapping and Synthetic Clinical Notes

Discharge summaries are free-text notes that outline the hospital stay of a patient, including diagnoses, treatments, and follow-up care recommendations. Through the use of International Classification of Diseases (ICD) coding, they play a vital role in communicating clinical data exchange and reimbursement. However, manually assigning ICD codes to discharge summaries can be labor-intensive and prone to errors due to the unstructured narrative format, variations in terminology, potential inaccuracies in documentation, and the limitations of the ICD coding system in capturing the complexity of patient conditions. This paper aims to improve the results of state-of-the-art automated medical coding machine learning models with two preprocessing techniques. The first technique involves data management to maximize the amount of usable data by mapping ICD-9 codes in the MIMIC-IV dataset to their corresponding ICD-10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>. With a similar goal, the second technique leverages large language models (LLMs) for the generation of multiple synthetic discharge summaries corresponding to ICD codes with few appearances in the dataset. The results of this extensive preprocessing are evaluated with the use of several well-established and state-of-the-art deep learning models, which present significant improvement.