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

Wei Qi Wei, Cui Tao, Guoqian D Jiang, Christopher G. Chute

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

UNLABELLED: 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.

Original languageEnglish (US)
Pages (from-to)857-861
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2010
StatePublished - 2010

ASJC Scopus subject areas

  • Medicine(all)

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