Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain (2020)

Abstract: Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only in isolation and does not study the effects of de-identification on other tasks. In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data and investigating joint models for de-identification and concept extraction. In particular, we propose a stacked model with restricted access to privacy sensitive information and a multitask model. We set the new state of the art on benchmark datasets in English (96.1% F1 for de-identification and 88.9% F1 for concept extraction) and Spanish (91.4% F1 for concept extraction).

## introduction in the clinical or biomedical domain, natural language processing (nlp) could significantly improve the efficiency and effectiveness of processes. for example, the extraction of structured information from clinical narratives can help in decision making or drug repurposing @xcite . however, the automatic processing of documents with privacy-sensitive content is restricted due to the necessity of applying anonymization techniques. text anonymization, also called deidentification, aims at detecting and replacing protected health information (phi), foot_0 such as patient names, age and phone numbers, as shown in the upper part of figure 1 . recent studies show that automatic de-identification leads to promising results @xcite . a severe limitation of current approaches in research, however, is that de-identification is typically addressed in isolation but not together with a downstream task, such as concept extraction (ce) from medical texts @xcite . instead, the downstream task models are trained and evaluated on the non-anonymized data, and it remains unclear how de-identification affects their performance in real-world settings. in this paper, we argue that to evaluate the effectiveness of nlp in the medical domain, the tasks of de-identification and information extraction should be analyzed together. our contributions are as follows: we close this gap and analyze the effect of de-identification on clinical concept extraction. moreover, we consider the two tasks jointly and propose two end-to-end models: a multitask model that shares the input representation across tasks, and a stacked model that trains a pipeline of de-identification and concept extraction in an end-to-end manner. for the stacked model, we propose to use a masked embedding layer to restrict the access of the concept detector to privacy-sensitive information and train it on an anonymized version of the data. to make the model differentiable, we use the gumbel softmax trick @xcite . we conduct experiments on clinical benchmark datasets in english and spanish. our results indicate that de-identification does not affect ce models negatively, but has even a slight positive effect for future research, we publish our code. foot_1 ## related work while many works propose joint training for other nlp tasks (i.a., @xcite , including multitask learning (i.a., @xcite @xcite and stacking of pipeline components (i.a., @xcite , we are to the best of our knowledge the first to combine de-identification with an information extraction task. in this section, we report related work in those two fields. ## model in this section, we present our systems for the two individual tasks and our proposed joint models. figure 2 shows the respective architectures. ## experiments in this section, we describe the datasets used in our experiments, and training details for our models. finally, we present our results and analysis. ## conclusion in this paper, we close the gap and consider deidentification of clinical text together with concept extraction, a possible downstream application. we investigate the effects of de-identification on concept extraction and show that it positively influences the concept extraction performance. we propose two models to learn both tasks jointly, a multitask model and a stacked model, and set the new state of the art on medical concept extraction benchmark datasets for english and spanish.