title: Applying active learning to high-throughput phenotyping algorithms for electronic health records data.
abstract: OBJECTIVE
  Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms.
  
  
  METHODS
  We integrated an uncertainty sampling AL approach with support vector machines-based phenotyping algorithms and evaluated its performance using three annotated disease cohorts including rheumatoid arthritis (RA), colorectal cancer (CRC), and venous thromboembolism (VTE). We investigated performance using two types of feature sets: unrefined features, which contained at least all clinical concepts extracted from notes and billing codes; and a smaller set of refined features selected by domain experts. The performance of the AL was compared with a passive learning (PL) approach based on random sampling.
  
  
  RESULTS
  Our evaluation showed that AL outperformed PL on three phenotyping tasks. When unrefined features were used in the RA and CRC tasks, AL reduced the number of annotated samples required to achieve an area under the curve (AUC) score of 0.95 by 68% and 23%, respectively. AL also achieved a reduction of 68% for VTE with an optimal AUC of 0.70 using refined features. As expected, refined features improved the performance of phenotyping classifiers and required fewer annotated samples.
  
  
  CONCLUSIONS
  This study demonstrated that AL can be useful in ML-based phenotyping methods. Moreover, AL and feature engineering based on domain knowledge could be combined to develop efficient and generalizable phenotyping methods.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, Tennessee, USA.
authors.forename: Yukun
authors.initials: Y
authors.lastname: Chen
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Carroll
authors.forename: Eugenia R McPeek
authors.initials: ER
authors.lastname: Hinz
authors.forename: Anushi
authors.initials: A
authors.lastname: Shah
authors.forename: Anne E
authors.initials: AE
authors.lastname: Eyler
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057286:Electronic Health Records; D056726:Genetic Association Studies; D006801:Humans; D010641:Phenotype; D060388:Support Vector Machine
keywords: Active Learning; Electronic Health Records; Machine Learning; Natural Language Processing; Phenotyping Algorithm