Explainable Clinical Decision Support from Text (2020)

Abstract: Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, free-text medical notes may contain information not immediately available in structured variables. We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively. We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis. Finally, we outline a protocol to evaluate model usability in a clinical decision support context. From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.

## introduction electronic medical records (emrs) store both structured data (e.g., vitals and laboratory measurements) and unstructured data (e.g., nursing and physician notes). previous clinical prediction tasks have focused on structured data (e.g., @xcite @xcite which, despite their utility, may not capture all of the useful information in associated text. clinical decision support systems rarely take advantage of free-text notes due to the complex nature of clinical language and interpretation. rules and specialized grammars can be applied to circumvent issues around clinical language; however, these methods rely on the presence of certain phrases and spelling, and do not account for the highly variable note structures across departments and hospitals @xcite @xcite . further, opaque models without explainability are often met with resistance in medical contexts @xcite @xcite . to address these challenges, we propose a novel multi-task language model that also provides rationales for decisions in medicine. our multi-task model leverages clinicalbert @xcite , which is a transformerbased model pre-trained on clinical corpora. given the uniqueness of medical text, we introduce a combination of cnn and transformer encoders to capture phrase-level patterns and global contextual relationships. additionally, we explore latent attention layers to generate rationales. based on availability, we use the mimic-iii database @xcite to predict two outcomes: sepsis and mortality in the intensive care unit (icu). all experiments are conducted on notes written in english. we define the task of sepsis prediction more rigorously than previous work due both to using textual data only, and to emphasize the practicality of this model in real-world applications. moreover, we use canonical correlation analysis (cca; hotelling 1992) to explore relationships between latent features learned from both structured and unstructured data. finally, we propose an evaluation protocol to examine the usability of our model as an interpretable decision support tool. ## experiments our experiments explore 1) differences in prediction due to pretraining, 2) multiview projection, and 3) evaluable explainable ai. ## conclusion language can provide valuable support to improve clinical decision-making. we conduct a diverse set of experiments to explore several aspects of the applicability of deep nlp in the clinical domain. we also address challenges in extracting medical documents that are representative of a predictive task. we augment the power of domain-specific bert and build a hierarchical cnn-transformer that can potentially be applied to any long-document processing task. the model achieves auroc scores of 0.75 and 0.78 on sepsis and mortality tasks, respectively. we also address model explainability by experimenting with a simple (yet effective) linear attention mechanism, and emphasize the interaction between models and users in the design of a novel protocol to evaluate explanations. not only are we able to sufficiently predict cases with performance comparable to models that use structured emr data, but we are also able to provide useful rationales to support the predictions, as validated by medical domain expertise. this has important implications for real-world application of explainable clinical decision support from text.