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Large-scale identification of patients with cerebral aneurysms using natural language processing.

Publication ,  Journal Article
Castro, VM; Dligach, D; Finan, S; Yu, S; Can, A; Abd-El-Barr, M; Gainer, V; Shadick, NA; Murphy, S; Cai, T; Savova, G; Weiss, ST; Du, R
Published in: Neurology
January 10, 2017

OBJECTIVE: To use natural language processing (NLP) in conjunction with the electronic medical record (EMR) to accurately identify patients with cerebral aneurysms and their matched controls. METHODS: ICD-9 and Current Procedural Terminology codes were used to obtain an initial data mart of potential aneurysm patients from the EMR. NLP was then used to train a classification algorithm with .632 bootstrap cross-validation used for correction of overfitting bias. The classification rule was then applied to the full data mart. Additional validation was performed on 300 patients classified as having aneurysms. Controls were obtained by matching age, sex, race, and healthcare use. RESULTS: We identified 55,675 patients of 4.2 million patients with ICD-9 and Current Procedural Terminology codes consistent with cerebral aneurysms. Of those, 16,823 patients had the term aneurysm occur near relevant anatomic terms. After training, a final algorithm consisting of 8 coded and 14 NLP variables was selected, yielding an overall area under the receiver-operating characteristic curve of 0.95. After the final algorithm was applied, 5,589 patients were classified as having aneurysms, and 54,952 controls were matched to those patients. The positive predictive value based on a validation cohort of 300 patients was 0.86. CONCLUSIONS: We harnessed the power of the EMR by applying NLP to obtain a large cohort of patients with intracranial aneurysms and their matched controls. Such algorithms can be generalized to other diseases for epidemiologic and genetic studies.

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Published In

Neurology

DOI

EISSN

1526-632X

Publication Date

January 10, 2017

Volume

88

Issue

2

Start / End Page

164 / 168

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Neurology & Neurosurgery
  • Natural Language Processing
  • Middle Aged
  • Male
  • Intracranial Aneurysm
  • International Classification of Diseases
  • Humans
  • Female
  • Electronic Health Records
 

Citation

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Castro, V. M., Dligach, D., Finan, S., Yu, S., Can, A., Abd-El-Barr, M., … Du, R. (2017). Large-scale identification of patients with cerebral aneurysms using natural language processing. Neurology, 88(2), 164–168. https://doi.org/10.1212/WNL.0000000000003490
Castro, Victor M., Dmitriy Dligach, Sean Finan, Sheng Yu, Anil Can, Muhammad Abd-El-Barr, Vivian Gainer, et al. “Large-scale identification of patients with cerebral aneurysms using natural language processing.Neurology 88, no. 2 (January 10, 2017): 164–68. https://doi.org/10.1212/WNL.0000000000003490.
Castro VM, Dligach D, Finan S, Yu S, Can A, Abd-El-Barr M, et al. Large-scale identification of patients with cerebral aneurysms using natural language processing. Neurology. 2017 Jan 10;88(2):164–8.
Castro, Victor M., et al. “Large-scale identification of patients with cerebral aneurysms using natural language processing.Neurology, vol. 88, no. 2, Jan. 2017, pp. 164–68. Pubmed, doi:10.1212/WNL.0000000000003490.
Castro VM, Dligach D, Finan S, Yu S, Can A, Abd-El-Barr M, Gainer V, Shadick NA, Murphy S, Cai T, Savova G, Weiss ST, Du R. Large-scale identification of patients with cerebral aneurysms using natural language processing. Neurology. 2017 Jan 10;88(2):164–168.

Published In

Neurology

DOI

EISSN

1526-632X

Publication Date

January 10, 2017

Volume

88

Issue

2

Start / End Page

164 / 168

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Neurology & Neurosurgery
  • Natural Language Processing
  • Middle Aged
  • Male
  • Intracranial Aneurysm
  • International Classification of Diseases
  • Humans
  • Female
  • Electronic Health Records