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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 …