Extractive Summarization of EHR Discharge Notes

Abstract

Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standardize notes, aid clinical decision making, and reduce medical errors. Here we provide an upper bound on extractive summarization of discharge notes and develop an LSTM model to sequentially label topics of history of present illness notes. We achieve an F1 score of 0.876, which indicates that this model can be employed to create a dataset for evaluation of extractive summarization methods.

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Cited by in corpus (5)

• Attention-based Clinical Note Summarization
• Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan
• Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?
• Characterizing the Value of Information in Medical Notes
• Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations