The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
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DOI：
10.1142/9789813279827_0003
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发表时间：
2018-08
影响因子：
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通讯作者：
D. Ding;C. Simpson;S. Pfohl;Dave C. Kale;Kenneth Jung;N. Shah
中科院分区：
文献类型：
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作者：
D. Ding;C. Simpson;S. Pfohl;Dave C. Kale;Kenneth Jung;N. Shah
电子表型是通过分析个人的医疗记录来确定个人是否具有感兴趣的医疗状况的任务，并且是临床信息学的基础。越来越多的电子表型通过监督学习进行。我们研究了多任务学习的有效性表型使用电子健康记录（EHR）数据。多任务学习旨在通过联合学习额外的辅助任务来提高目标任务的模型性能，并已用于机器学习的不同领域。然而，它的效用时，应用于电子健康记录数据尚未建立，以前的工作表明，其好处是不一致的。我们提出的实验，阐明了当多任务学习与神经网络提高性能的表型使用EHR数据相对于神经网络训练一个单一的表型和良好的调整基线。我们发现，多任务神经网络在罕见的表型上始终优于单任务神经网络，但在相对更常见的表型上表现不佳。随着辅助任务的增加，效应量也随之增加。此外，多任务学习降低了神经网络对罕见表型的超参数设置的敏感性。最后，我们量化了表型的复杂性，并发现使用或不使用多任务学习训练的神经网络不会在简单的基线上有所改善，除非表型足够复杂。
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational 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 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. We present experiments that elucidate when multitask learning with neural nets improves performance for phenotyping using EHR data relative to neural nets trained for a single phenotype and to well-tuned baselines. We find that multitask neural nets consistently outperform single-task neural nets for rare phenotypes but underperform for relatively more common phenotypes. The effect size increases as more auxiliary tasks are added. Moreover, multitask learning reduces the sensitivity of neural nets to hyperparameter settings for rare phenotypes. Last, we quantify phenotype complexity and find that neural nets trained with or without multitask learning do not improve on simple baselines unless the phenotypes are sufficiently complex.