title: A computational framework for converting textual clinical diagnostic criteria into the quality data model.
abstract: Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria.
  
  
  
  To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM.
  
  
  
  We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation.
  
  
  
  We harvested 267 individual criteria with the datatypes of Symptom and Laboratory Test from 63 textual diagnostic criteria. We manually annotated attributes and values in 142 individual Laboratory Test criteria. The average performance of our rule-based approach was 0.84 of precision, 0.86 of recall, and 0.85 of f-measure; the performance of CRFs-based classification was 0.95 of precision, 0.88 of recall and 0.91 of f-measure. We also implemented a web-based tool that automatically translates textual Laboratory Test criteria into the QDM XML template format. The results indicated that our approaches leveraging cTAKES and CRFs are effective in facilitating diagnostic criteria annotation and classification.
  
  
  
  Our NLP-based computational framework is a feasible and useful solution in developing diagnostic criteria representation and computerization.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Na
authors.initials: N
authors.lastname: Hong
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Dingcheng
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Medical Informatics, School of Public Health, Jilin University, Changchun, Jilin, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Yu
authors.affiliation: Computer Science, Winona State University, Rochester, MN, USA.
authors.forename: Qiongying
authors.initials: Q
authors.lastname: Xiu
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Electronic address: Jiang.Guoqian@mayo.edu.
authors.forename: Guoqian
authors.initials: G
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D000068598:Data Accuracy; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing
keywords: Conditional random fields; Diagnostic criteria; Natural language processing; Quality data model; cTAKES