title: A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes.
abstract: Current research on high throughput identification of patients with a specific phenotype is in its infancy. There is an urgent need to develop a general automatic approach for patient identification.
  
  
  OBJECTIVE
  We took advantage of Mayo Clinic electronic clinical notes and proposed a novel method of combining NLP, machine learning, and ontology for automatic patient identification. We also investigated the benefits of involving existing SNOMED semantic knowledge in a patient identification task.
  
  
  METHODS
  the SVM algorithm was applied on SNOMED concept units extracted from T2DM case/control clinical notes. Precision, recall, and F-score were calculated to evaluate the performance.
  
  
  RESULTS
  This approach achieved an F-score of above 0.950 for both groups when using all identified concept units as features. Concept units from semantic type-Disease or Syndrome contain the most important information for patient identification. Our results also implied that the coarse level concepts contain enough information to classify T2DM cases/controls.
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Wei-Qi
authors.initials: WQ
authors.lastname: Wei
authors.forename: Cui
authors.initials: C
authors.lastname: Tao
authors.forename: Guoqian
authors.initials: G
authors.lastname: Jiang
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
mesh_terms: D000465:Algorithms; D003924:Diabetes Mellitus, Type 2; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D012660:Semantics; D039061:Systematized Nomenclature of Medicine