An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records (2024)

Abstract: Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantly enhanced the processing of such records, streamlining workflows and reducing manual data entry, thereby saving healthcare providers significant resources. However, the black-box nature of these models often leaves healthcare professionals hesitant to trust them. State-of-the-art explainability methods increase model transparency but rely on human-annotated evidence spans, which are costly. In this study, we propose an approach to produce plausible and faithful explanations without needing such annotations. We demonstrate on the automated medical coding task that adversarial robustness training improves explanation plausibility and introduce AttInGrad, a new explanation method superior to previous ones. By combining both contributions in a fully unsupervised setup, we produce explanations of comparable quality, or better, to that of a supervised approach. We release our code and model weights.

## introduction explainability in natural language processing remains a largely unsolved problem, posing significant challenges for healthcare applications @xcite . for every patient admission, a healthcare professional must read extensive documentation in the healthcare records to assign appropriate medical codes. a code is a machine-readable identifier for a diagnosis or procedure, pivotal for tasks such as statistics, documentation, and billing. this process can involve sifting through thousands of words to choose from over 140,000 possible codes @xcite , making med-63-year-old female with a history of asthma, cad, dmii presenting from an osh with septic shock found to have mssa endocarditis. septic shock: mssa bacteremia (osh with 4/4 bottles of blood cultures positive) and e. coli in the urine. ## methods here, we describe the adversarial robustness training strategies and feature attribution methods in the context of a prediction model for medical coding. the underlying automated medical coding model takes a sequence of tokens as input and outputs medical code probabilities (section 4.2). ## experimental setup in the following, we present our datasets, models, and evaluation metrics. ## results next, we present experimental results for the different training strategies and explainability methods. ## conclusion our goal was to enhance the plausibility and the faithfulness of explanations without evidence-span annotations. we found that training our model using input gradient regularization or token masking resulted in more plausible gradient-based explanations. we proposed a new explanation method, attingrad, which was substantially more plausible and faithful than the attention-based explanation method used in previous studies. by combining the best training strategy and explanation method, we showed results of similar quality to a supervised baseline @xcite .