Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

@article{Yu2015TowardHP,
  title={Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources},
  author={Sheng Yu and Katherine P. Liao and Stanley Y. Shaw and Vivian S. Gainer and Susanne E. Churchill and Peter Szolovits and Shawn N. Murphy and Isaac S. Kohane and Tianxi Cai},
  journal={Journal of the American Medical Informatics Association : JAMIA},
  year={2015},
  volume={22 5},
  pages={993-1000}
}
OBJECTIVE Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert… CONTINUE READING
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