title: Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text.
abstract: OBJECTIVE
  To develop an automated method for extracting and structuring numeric lab test comparison statements from text and evaluate the method using clinical trial eligibility criteria text.
  
  
  METHODS
  Leveraging semantic knowledge from the Unified Medical Language System (UMLS) and domain knowledge acquired from the Internet, Valx takes seven steps to extract and normalize numeric lab test expressions: 1) text preprocessing, 2) numeric, unit, and comparison operator extraction, 3) variable identification using hybrid knowledge, 4) variable - numeric association, 5) context-based association filtering, 6) measurement unit normalization, and 7) heuristic rule-based comparison statements verification. Our reference standard was the consensus-based annotation among three raters for all comparison statements for two variables, i.e., HbA1c and glucose, identified from all of Type 1 and Type 2 diabetes trials in ClinicalTrials.gov.
  
  
  RESULTS
  The precision, recall, and F-measure for structuring HbA1c comparison statements were 99.6%, 98.1%, 98.8% for Type 1 diabetes trials, and 98.8%, 96.9%, 97.8% for Type 2 diabetes trials, respectively. The precision, recall, and F-measure for structuring glucose comparison statements were 97.3%, 94.8%, 96.1% for Type 1 diabetes trials, and 92.3%, 92.3%, 92.3% for Type 2 diabetes trials, respectively.
  
  
  CONCLUSIONS
  Valx is effective at extracting and structuring free-text lab test comparison statements in clinical trial summaries. Future studies are warranted to test its generalizability beyond eligibility criteria text. The open-source Valx enables its further evaluation and continued improvement among the collaborative scientific community.
authors.forename: Tianyong
authors.initials: T
authors.lastname: Hao
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
authors.affiliation: Chunhua Weng, Ph.D., Department of Biomedical Informatics, Columbia University, New York City, 622 W 168th Street, PH-20, New York, NY 10032, USA, E-mail: chunhua@columbia.edu.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D000465:Algorithms; D057225:Data Mining; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D009716:Numerical Analysis, Computer-Assisted; D014584:User-Computer Interface
chemical_list: D006442:Glycated Hemoglobin
keywords: Medical informatics; clinical trial; comparison statement; natural language processing; patient selection