Data Augmentation for Radiology Report Simplification (2023)

Abstract: This work considers the development of a text simplification model to help patients better understand their radiology reports. This paper proposes a data augmentation approach to address the data scarcity issue caused by the high cost of manual simplification. It prompts a large foundational pre-trained language model to generate simplifications of unlabeled radiology sentences. In addition, it uses paraphrasing of labeled radiology sentences. Experimental results show that the proposed data augmentation approach enables the training of a significantly more accurate simplification model than the baselines.

## introduction radiology reports are unstructured documents written by radiologists to communicate imaging findings to another physician or a qualified medical professional @xcite . radiology reports have been increasingly available to patients through portals (lourenco and baird, 2020), which has been generally welcomed by patients @xcite . however, the health literacy of most patients is insufficient to fully comprehend radiology reports @xcite because such reports rely on complex medical jargon and use explanations that imply highly specialized medical knowledge @xcite . several studies even identified adverse effects of sharing radiology reports with patients, including dissatisfaction with care @xcite and undue anxiety and stress @xcite . there is an increasing need for patient-friendly radiology reporting that can communicate results clearly and be understandable by a diverse patient population. however, asking a radiologist to supplement a traditional report with a patient-friendly summary would negatively impact their cognitive load and productivity. this problem motivated recent research on the automatic simplification of health records. the proposed approaches include both lexical simplification that paraphrases text @xcite @xcite and semantic simplification that seeks to simplify grammatically complex text @xcite which recently included deep learning approaches @xcite . however, training deep learning models for medical text simplification requires the collection of costly labeled data. to alleviate the data scarcity issue in simplifying health reports, particularly radiology reports, this paper proposes a novel approach for data augmentation. it augments manually-created labeled data with simplifications generated by a large pretrained language model such as @xcite . to improve the quality of data augmentation, the approach develops a separate deep learning model that evaluates the quality of generated simplifications. furthermore, the approach also provides data augmentation through paraphrasing the originally labeled radiology sentences. the proposed data augmentation approach is experimentally evaluated on a unique corpus of manually generated labeled data for radiology report simplification. the evaluation includes both automatic measures and human evaluation. our research claims are: 1) our augmentation methods enable training of a more accurate model than baselines in solving low-resource radiology sentence simplification problems. 2) we address the challenge of selecting qualified augmentations for radiology sentence simplification. 3) we create unique real data containing expert-annotated simplifications for radiology reports' sentences regarding liver conditions. lexical simplification by lexical substitution refers to replacing complex words or phrases with simpler synonyms @xcite and has found some practical success @xcite . in the health domain, lexical text simplification often relies on medical dictionaries (umls @xcite , mesh @xcite , etc.). lexical simplification approaches also include rule-based methods @xcite and deep learning @xcite . semantic simplifications seek to simplify grammatically complex text by splitting long sentences into shorter ones, changing passive voice to active, resolving ambiguities and anaphora (shardlow, 2014), splitting complex noun phrases @xcite , or reducing morphological negations @xcite . recently, transformer encoder-decoder based pre-trained seq-to-seq models @xcite were proved to be robust in solving text simplification problems. however, fine-tuning pre-trained models require large quantities of labeled data, which are costly and difficult to obtain in the health domain. previous research has explored different methods for text simplification in low-resource domains. to address data scarcity recent studies include unsupervised methods @xcite @xcite and reinforcement learning @xcite . data augmentation is a method that automatically generates labeled data to enhance manually labeled data @xcite . one approach is to use paraphrasing to create different variants of the original or simplified sentences @xcite . another approach is to use pre-trained language models to generate labeled data @xcite . lambada (anaby-tavor et al., 2020) augments data for text classification tasks by encoding labels in the input. similarly, promptda @xcite use language models to augment data for nlu tasks. back-translation @xcite is used to generate different variants of the input text. there are several public benchmark data sets that are related to our paper. there are paragraph level medical text simplifications @xcite focusing on medical paper abstracts. there is a corpus parsed aligned sentences from wikipedia and simple english wikipedia foot_0 @xcite that has been a popular text simplification benchmark. however, none of these data sets have properties similar to the radiology text simplification task. ## conclusion this paper proposes two novel augmentation methods to enhance the limited labeled data for the radiology sentence simplification problem. our evaluation using automatic measures and human evaluation shows that data augmentation can substantially improve the quality of simplification models. the ablation results show that the proposed innovations in automatic creation of simplifications for data augmentation are very effective.