Nov 11, 2017

Enabling phenotypic big data with PheNorm

Journal of the American Medical Informatics Association : JAMIA
Sheng YuTianxi Cai

Abstract

Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score...Continue Reading

  • References36
  • Citations16

References

  • References36
  • Citations16

Citations

Mentioned in this Paper

Area Under Curve
Study
Electronic Health Records
Classification
Coronary Artery Disease
Coronary Arteriosclerosis
Rheumatoid Arthritis
High Throughput Analysis
Individualized Medicine
Polypeptides

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