A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes

AMIA Annu Symp Proc. 2010 Nov 13;2010:857-61.


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.

MeSH terms

  • Algorithms
  • Diabetes Mellitus, Type 2
  • Electronic Health Records
  • Humans
  • Natural Language Processing*
  • Semantics*
  • Systematized Nomenclature of Medicine