title: Graph-based signal integration for high-throughput phenotyping.
abstract: BACKGROUND
  Electronic Health Records aggregated in Clinical Data Warehouses (CDWs) promise to revolutionize Comparative Effectiveness Research and suggest new avenues of research. However, the effectiveness of CDWs is diminished by the lack of properly labeled data. We present a novel approach that integrates knowledge from the CDW, the biomedical literature, and the Unified Medical Language System (UMLS) to perform high-throughput phenotyping. In this paper, we automatically construct a graphical knowledge model and then use it to phenotype breast cancer patients. We compare the performance of this approach to using MetaMap when labeling records.
  
  
  RESULTS
  MetaMap's overall accuracy at identifying breast cancer patients was 51.1% (n=428); recall=85.4%, precision=26.2%, and F1=40.1%. Our unsupervised graph-based high-throughput phenotyping had accuracy of 84.1%; recall=46.3%, precision=61.2%, and F1=52.8%.
  
  
  CONCLUSIONS
  We conclude that our approach is a promising alternative for unsupervised high-throughput phenotyping.
authors: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu
authors: Jorge R
authors: JR
authors: Herskovic
authors: Devika
authors: D
authors: Subramanian
authors: Trevor
authors: T
authors: Cohen
authors: Pamela A
authors: PA
authors: Bozzo-Silva
authors: Charles F
authors: CF
authors: Bearden
authors: Elmer V
authors: EV
authors: Bernstam
mesh_terms: D001943:Breast Neoplasms / Q000145:classification*; D003198:Computer Simulation; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008954:Models, Biological; D010641:Phenotype; D017432:Unified Medical Language System