Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data

• URL: http://arxiv.org/abs/2103.00482v1
• Date: Wed, 24 Feb 2021 18:18:02 GMT
• Title: Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data
• Authors: Yuqi Si, Elmer V Bernstam, Kirk Roberts
• Abstract summary: We propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes.
• Score: 5.767430988202727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 respiratory diseases, and 17 genitourinary diseases. We find multi-task pre-training increases learning efficiency and achieves consistently high performance across the majority of phenotypes. Most important, the multi-task pre-training is almost always either the best-performing model or performs tolerably close to the best-performing model, a property we refer to as robust. All these results lead us to conclude that this multi-task transfer learning architecture is a robust approach for developing generalized and transferable patient language representations for numerous phenotypes.

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