Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
Abstract
Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs). However, the training of a supervised model requires disease-specific medical background and is thus very expensive. We studied how to utilize the intrinsic correlation between multiple EHRs to generate pseudo-labels and train a supervised model with no external annotation. Experiments on real-patient data validate that our model is effective in summarizing crucial disease-specific information for patients.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2018
- DOI:
- 10.48550/arXiv.1811.08040
- arXiv:
- arXiv:1811.08040
- Bibcode:
- 2018arXiv181108040L
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216