The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
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Electronic phenotyping, which is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical records, is a foundational task in clinical informatics. Increasingly, electronic phenotyping is performed via supervised learning. We investigate the effectiveness of multitask learning for phenotyping using electronic health records (EHR) data. Multitask learning aims to improve model performance on a target task by jointly learning additional auxiliary tasks, and has been used to good effect in disparate areas of machine learning. However, its utility when applied to EHR data has not been established, and prior work suggests that its benefits are inconsistent. Here we present experiments that elucidate when multitask learning with neural networks can improve performance for electronic phenotyping using EHR data relative to well-tuned single task neural networks. We find that multitask networks consistently outperform single task networks for rare phenotypes but underperform for more common phenotypes. The effect size increases as more auxiliary tasks are added.
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