Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes? (2022)

Abstract: The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods. However, most previous SOTA works based on deep learning are still in early stages in terms of providing textual references and explanations of the predicted codes, despite the fact that this level of explainability of the prediction outcomes is critical to gaining trust from professional medical coders. This raises the important question of how well current explainability methods apply to advanced neural network models such as transformers to predict correct codes and present references in clinical notes that support code prediction. First, we present an explainable Read, Attend, and Code (xRAC) framework and assess two approaches, attention score-based xRAC-ATTN and model-agnostic knowledge-distillation-based xRAC-KD, through simplified but thorough human-grounded evaluations with SOTA transformer-based model, RAC. We find that the supporting evidence text highlighted by xRAC-ATTN is of higher quality than xRAC-KD whereas xRAC-KD has potential advantages in production deployment scenarios. More importantly, we show for the first time that, given the current state of explainability methodologies, using the SOTA medical codes prediction system still requires the expertise and competencies of professional coders, even though its prediction accuracy is superior to that of human coders. This, we believe, is a very meaningful step toward developing explainable and accurate machine learning systems for fully autonomous medical code prediction from clinical notes.

## introduction within current medical systems, the prediction of medical codes from clinical notes is a practical and essential need for every healthcare delivery organi-zation @xcite . a human coder or health care provider scans medical documentation in electronic health records, identifying important information and annotating codes for that specific treatment or service. with a diverse range of medical services and providers (primary care clinics, specialty clinics, emergency departments, mother-baby units, outpatient and inpatient units, etc.), the complexity of human coders' tasks grows, while productivity standards fall as charts take longer to review. thus, even partial automation of the annotation workflow will save significant time and effort that human coders currently spend. the biggest challenge, however, is directly identifying appropriate medical codes from thousands of high-dimensional codes from unstructured free-text clinical notes @xcite . lately, advanced deep learning-based methods for predicting medical codes based on clinical notes @xcite @xcite have achieved state-of-the-art prediction performance and even reached parity with human coders' performance @xcite . however, most current works on medical code prediction based on deep learning models do not provide the end-user with references from the clinical notes to explain why the predicted codes were presented/chosen. there have been some related works that provide the rationales or text highlights from clinical notes to explain why the predictions were made to support humans clinical decision making @xcite @xcite . however, to the best of our knowledge, there is still a gap in studies that have thoroughly analyzed explainability to extract supporting text for code prediction, especially made by state-of-the-art (sota) transformer-based models such as the rac model @xcite . two examples are the attention score-based ap-proach first introduced in @xcite and the model-independent knowlege-distillation based method recently initiated in @xcite . the first approach utilizes the perlabel attention mechanism to select key sentences for prediction decisions; however, if the model does not have the per-label attention layer, it cannot generate text snippets, and even worse, applying this method to transformer-based architecture and deploying it to production comes with a range of compute and memory challenges @xcite . in the second knowledge-distillation approach, a large neural network is distilled into linear student models in a post-hoc manner without sacrificing much accuracy of the teacher model while retaining many advantages of linear models including explainability and smaller model size, which is beneficial for deployment. this paper makes two primary contributions: ## conclusion in this paper, a xrac framework is presented to obtain supporting evidence text from clinical notes that justify the predicted medical codes from medical code prediction systems. we have demonstrated that the proposed xrac framework may help even complex transformer-based models (e.g., rac model) to attain high accuracy with a decent level of explainability (which is of high value for deployment scenarios) through quantitative experimental studies and qualitative human-grounded evaluations. it was also shown for the first time that, given the current state of explainability methodologies, using the proposed explainable yet accurate medical codes prediction system still requires professional coders' expertise and competencies.