Applying active learning to high-throughput phenotyping algorithms for electronic health records data.

@article{Chen2013ApplyingAL,
  title={Applying active learning to high-throughput phenotyping algorithms for electronic health records data.},
  author={Yukun Chen and Robert J. Carroll and Eugenia R. McPeek Hinz and Anushi Shah and Anne E. Eyler and Joshua C. Denny and Hua Xu},
  journal={Journal of the American Medical Informatics Association : JAMIA},
  year={2013},
  volume={20 e2},
  pages={
          e253-9
        }
}
OBJECTIVES Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms. METHODS We integrated an uncertainty sampling AL approach with support… CONTINUE READING

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