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Surrogate-assisted feature extraction for high-throughput phenotyping
- Source :
- Journal of the American Medical Informatics Association : JAMIA. 24(e1)
- Publication Year :
- 2016
-
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’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’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.
- Subjects :
- 0301 basic medicine
Computer science
Feature extraction
Health Informatics
Feature selection
Overfitting
computer.software_genre
Machine learning
Research and Applications
ComputingMethodologies_ARTIFICIALINTELLIGENCE
Set (abstract data type)
Machine Learning
03 medical and health sciences
Robustness (computer science)
Data Mining
Electronic Health Records
Humans
Throughput (business)
Natural Language Processing
Receiver operating characteristic
business.industry
030104 developmental biology
ComputingMethodologies_PATTERNRECOGNITION
Phenotype
Scalability
Data mining
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 1527974X
- Volume :
- 24
- Issue :
- e1
- Database :
- OpenAIRE
- Journal :
- Journal of the American Medical Informatics Association : JAMIA
- Accession number :
- edsair.doi.dedup.....a6cf43f5f90ec81e29f8ddbc2a719689