Semi-supervised Learning for Phenotyping Tasks

• Published in: AMIA ... Annual Symposium proceedings Vol. 2015; pp. 502 - 511
• Main Authors: Dligach, Dmitriy, Miller, Timothy, Savova, Guergana K
• Format: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2015
• Subjects: Algorithms; Datasets as Topic; Disease - classification; Electronic Health Records; Humans; Information Storage and Retrieval - methods; Supervised Machine Learning
• ISSN: 1942-597X; 1559-4076

Supervised learning is the dominant approach to automatic electronic health records-based phenotyping, but it is expensive due to the cost of manual chart review. Semi-supervised learning takes advantage of both scarce labeled and plentiful unlabeled data. In this work, we study a family of semi-supervised learning algorithms based on Expectation Maximization (EM) in the context of several phenotyping tasks. We first experiment with the basic EM algorithm. When the modeling assumptions are violated, basic EM leads to inaccurate parameter estimation. Augmented EM attenuates this shortcoming by introducing a weighting factor that downweights the unlabeled data. Cross-validation does not always lead to the best setting of the weighting factor and other heuristic methods may be preferred. We show that accurate phenotyping models can be trained with only a few hundred labeled (and a large number of unlabeled) examples, potentially providing substantial savings in the amount of the required manual chart review.