Towards Explainability in Automated Medical Code Prediction from Clinical Records

The International Statistical Classification of Diseases and Related Health Problems (ICD) is a global standard, a diagnostic tool that is frequently used for endemic research, health management, and clinical diagnosis, and it plays a crucial role in providing shrewd medical treatment. Comparable statistics on the causes of mortality and morbidity across locations and throughout time have been based on the ICD. The traditional procedure of assigning codes is expensive, error-prone and time-consuming, and automated mapping of ICD codes is now a significant area of scholarly research. With the help of statistical modeling, rule-engines, conventional machine learning, and deep learning techniques like graph embedding, attention mechanisms, adversarial learning, and pre-trained language models (PLMs), this paper aims to analyze and document inferences on the evolution of clinical coding automation. We try to summarize with comparative performance analysis various approaches addressed towards codification of free-text clinical narratives on the publicly available Medical Information Mart. This study investigates whether clinicians and researchers could benefit from an adequate interpretation of model predictions from an Explainable Artificial Intelligence (XAI) perspective. Finally, the survey illustrates ICD coding and disease classification applications and its challenges, evaluation metrics, datasets, and directions towards automating explanatory medical code predictions.