Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation (2022)

Abstract: Patients with low health literacy usually have difficulty understanding medical jargon and the complex structure of professional medical language. Although some studies are proposed to automatically translate expert language into layperson-understandable language, only a few of them focus on both accuracy and readability aspects simultaneously in the clinical domain. Thus, simplification of the clinical language is still a challenging task, but unfortunately, it is not yet fully addressed in previous work. To benchmark this task, we construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. Besides, we propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance compared with eight strong baselines. To fairly evaluate the performance, we also propose three specific evaluation metrics. Experimental results demonstrate the utility of the annotated MedLane dataset and the effectiveness of the proposed model DECLARE.

## introduction health literacy is generally defined as the ability of patients to obtain, process, understand, and communicate basic health information @xcite , which is significantly important for making health decisions and ensuring treatment outcomes. the increasing accessibility of technology information makes patients have more opportunities to access health information. however, it is challenging for patients, especially with limited health literacy, to read and understand health materials such as clinical notes written by doctors, with medical jargon @xcite , abbreviations, and professional language @xcite . the lack of proper communication between doctors and patients not only results in a tense doctorpatient relationship @xcite but also increases the risk of adverse health outcomes over time @xcite . therefore, there is a great need to simplify professional clinical language to layperson-understandable language. ## declare: an effective approach motivated by the annotation steps, we propose an effective end-to-end model named declare for the automated clinical language simplification task. in particular, we collect a medical dictionary to estimate the possible full-term expressions of abbreviations in the input sentences. the proposed model is shown in figure 2 , which consists of three main modules, i.e., a complex word locator (cwl), a dictionary-based neural lexical interpreter (dnli), and a restricted syntactic simplification polisher (rssp). given a tokenized professional medical sentence @xmath0, w 2 , • • • , w n ], where n denotes the number of tokens, the locator aims to dig out possible phrases that need to be simplified or translated. in the neural lexical interpreter, the chosen phrases will be replaced with full-term expressions selected from the medical dictionary. finally, the replaced sentence will pass the polisher to generate the final output y . these three parts tightly work together and enhance each other. next, we introduce the design details of each module, respectively. ## conclusion this paper aims to benchmark a new, challenging, yet practical task of automated clinical language simplification by constructing a high-quality medlane dataset and proposing a new model de-clare that mimics the human annotation procedure. we conducted experiments on the annotated medlane dataset by implementing nine strong baselines against declare. experimental results confirmed the utility of the constructed medlane dataset and the effectiveness of the proposed de-clare for addressing the automated clinical language simplification task.