Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
arXiv:1811.08040
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.
arXiv commentMachine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
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