Extractive Summarization of EHR Discharge Notes
This work addresses the problem of automated summarization for clinicians to improve care coordination, but it is incremental as it focuses on dataset creation rather than a new summarization method.
The study tackled extractive summarization of EHR discharge notes by establishing an upper bound and developing an LSTM model for topic labeling, achieving an F1 score of 0.876 to create a dataset for method evaluation.
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.