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arXiv · 1811.08040

Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records

Xiangan Liu·Keyang Xu·Pengtao Xie·Eric Xing
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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.

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Xiangan Liu, Keyang Xu, Pengtao Xie, Eric Xing. 2018-11-20. Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records. https://arxiv.org/abs/1811.08040

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