@article {1003876,
	title = {Surrogate-assisted feature extraction for high-throughput phenotyping},
	journal = {J Am Med Inform Assoc},
	year = {2016},
	month = {2016 Sep 15},
	abstract = {OBJECTIVE: Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy.

METHODS: The proposed Surrogate-Assisted Feature Extraction (SAFE) method selects candidate features from a pool of comprehensive medical concepts found in publicly available knowledge sources. The target phenotype{\textquoteright}s International Classification of Diseases, Ninth Revision and natural language processing counts, acting as noisy surrogates to the gold-standard labels, are used to create silver-standard labels. Candidate features highly predictive of the silver-standard labels are selected as the final features.

RESULTS: Algorithms were trained to identify patients with coronary artery disease, rheumatoid arthritis, Crohn{\textquoteright}s disease, and ulcerative colitis using various numbers of labels to compare the performance of features selected by SAFE, a previously published automated feature extraction for phenotyping procedure, and domain experts. The out-of-sample area under the receiver operating characteristic curve and F-score from SAFE algorithms were remarkably higher than those from the other two, especially at small label sizes.

CONCLUSION: SAFE advances high-throughput phenotyping methods by automatically selecting a succinct set of informative features for algorithm training, which in turn reduces overfitting and the needed number of gold-standard labels. SAFE also potentially identifies important features missed by automated feature extraction for phenotyping or experts.},
	issn = {1527-974X},
	doi = {10.1093/jamia/ocw135},
	author = {Yu, Sheng and Chakrabortty, Abhishek and Liao, Katherine P and Cai, Tianrun and Ananthakrishnan, Ashwin N and Gainer, Vivian S and Churchill, Susanne E and Szolovits, Peter and Murphy, Shawn N and Kohane, Isaac S and Cai, Tianxi}
}
