Enabling phenotypic big data with PheNorm

Abstract

Objective: 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.

Methods: 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.

Results: 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 reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference.

Conclusion: The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data.

Keywords: electronic health records; high-throughput phenotyping; phenotypic big data; precision medicine.

Figure 1.

Workflow of PheNorm. Top left: density plot (after logarithm transformation) of a highly predictive feature (illustrated here using the ICD-9-CM count of ulcerative colitis from a Partners HealthCare EHR datamart), denoted by x, in patients who do (the right curve) and do not (the left curve) have the phenotype. Top right: Density plot of the ICD-9-CM count after the normal mixture transformation using the total number of notes in the patient’s EHR, denoted by x_note. The densities of the phenotype positive and negative patients are approximately normally distributed, and the 2 populations are separated to a large degree. Bottom right: The transformed feature is denoised by self-regression of the transformed feature, denoted by y, onto the entire transformed and randomly corrupted feature set, denoted by Z˜, with dropout. The transformed features are then combined into a prediction formula for disease status classification based on the estimated regression coefficient. Bottom left: The receiver operating characteristic (ROC) curve of the feature or score in each step, with AUC growing steadily (gray curves are copies of the ROC curves from the previous steps).