Evaluating semantic textual similarity in clinical sentences using deep learning and sentence embeddings

Abstract

The wide adoption of electronic health records (EHRs) has fostered an improvement in healthcare quality, with EHRs currently representing a major source of medical information. Nevertheless, this process has also brought new challenges to the medical environment since the facilitated replication of information (e.g. using copy-paste) has resulted in less concise and sometimes incorrect information, which hinders the understandability of this data and can compromise the quality of medical decisions drawn from it. Due to the high volume and redundancy in medical data, it is imperative to develop solutions that can condense information whilst retaining its value, with a possible methodology involving the assessment of the semantic similarity between clinical text excerpts. In this paper we present an approach that explores neural networks and different types of text preprocessing pipelines, and that evaluates the impact of using word embeddings or sentence embeddings. We present the results following our participation in the n2c2 shared-task on clinical semantic textual similarity, perform an error analysis and discuss obtained results along with possible future improvements.