Abstract

BackgroundWith the popularity of electronic health records (EHRs), the quality of health care has been improved. However, there are also some problems caused by EHRs, such as the growing use of copy-and-paste and templates, resulting in EHRs of low quality in content. In order to minimize data redundancy in different documents, Harvard Medical School and Mayo Clinic organized a national natural language processing (NLP) clinical challenge (n2c2) on clinical semantic textual similarity (ClinicalSTS) in 2019. The task of this challenge is to compute the semantic similarity among clinical text snippets.ObjectiveIn this study, we aim to investigate novel methods to model ClinicalSTS and analyze the results.MethodsWe propose a semantically enhanced text matching model for the 2019 n2c2/Open Health NLP (OHNLP) challenge on ClinicalSTS. The model includes 3 representation modules to encode clinical text snippet pairs at different levels: (1) character-level representation module based on convolutional neural network (CNN) to tackle the out-of-vocabulary problem in NLP; (2) sentence-level representation module that adopts a pretrained language model bidirectional encoder representation from transformers (BERT) to encode clinical text snippet pairs; and (3) entity-level representation module to model clinical entity information in clinical text snippets. In the case of entity-level representation, we compare 2 methods. One encodes entities by the entity-type label sequence corresponding to text snippet (called entity I), whereas the other encodes entities by their representation in MeSH, a knowledge graph in the medical domain (called entity II).ResultsWe conduct experiments on the ClinicalSTS corpus of the 2019 n2c2/OHNLP challenge for model performance evaluation. The model only using BERT for text snippet pair encoding achieved a Pearson correlation coefficient (PCC) of 0.848. When character-level representation and entity-level representation are individually added into our model, the PCC increased to 0.857 and 0.854 (entity I)/0.859 (entity II), respectively. When both character-level representation and entity-level representation are added into our model, the PCC further increased to 0.861 (entity I) and 0.868 (entity II).ConclusionsExperimental results show that both character-level information and entity-level information can effectively enhance the BERT-based STS model.

Highlights

  • BackgroundElectronic health record (EHR) systems have been widely used in hospitals all over the world for convenience to health information storage, share, and exchange [1]

  • This can be regarded as a clinical semantic textual similarity (ClinicalSTS) task, which is applied to clinical decision support, trial recruitment, tailored care, clinical research [4,5,6], and medical information services, such as clinical question answering [7,8] and document classification [9]

  • The n2c2/Open Health NLP (OHNLP) organizers manually annotated a total of 2055 clinical text snippet pairs by 2 medical experts for the ClinicalSTS task, where 1643 pairs are used as the training set and 412 as the test set

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Summary

Introduction

BackgroundElectronic health record (EHR) systems have been widely used in hospitals all over the world for convenience to health information storage, share, and exchange [1]. In order to minimize data redundancy in different documents, Harvard Medical School and Mayo Clinic organized a national natural language processing (NLP) clinical challenge (n2c2) on clinical semantic textual similarity (ClinicalSTS) in 2019. The task of this challenge is to compute the semantic similarity among clinical text snippets. Methods: We propose a semantically enhanced text matching model for the 2019 n2c2/Open Health NLP (OHNLP) challenge on ClinicalSTS. Conclusions: Experimental results show that both character-level information and entity-level information can effectively enhance the BERT-based STS model

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