Semantic Similarity of Clinical Texts

In the modern world of increasingly digitized healthcare infrastructure, clinical notes and other reports are maintained digitally. They are, however, almost always manually created. This has resulted in pervasive copy-paste actions across the board, leading to an immense amount of redundant information in such notes and reports. Using an ensemble of traditional ontology-based methods and state-of-the-art neural networks, a lightweight but highly accurate system was developed to detect clinical texts for semantic duplication and similarity [Salek Faramarzi et al. 2022].

Research Group

• Ritwik Banerjee, Research Assistant Professor of Computer Science, Stony Brook University

• Noushin Salek Faramarzi, Research Assistant

• Akanksha Dara, M.S. ↦ Software Engineer, Apple Inc.

Publications

• Noushin Salek Faramarzi, Akanksha Dara and Ritwik Banerjee. Combining Attention-based Models with the MeSH Ontology for Semantic Textual Similarity in Clinical Notes. In IEEE 10th International Conference on Healthcare Informatics (ICHI), pp. 74 - 83. IEEE, 2022. DOI: ICHI54592.2022.00023 [ PDF ]