Large-scale identification of patients with cerebral aneurysms using natural language processing.
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...
| Publicado en: | Neurology Vol. 88; no. 2; pp. 164 - 169 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
1/10/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120651482&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120651482 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283878 NRO jtl: Neurology issn: 00283878 maglogo: N pubinfo: dt: 1/10/2017 vid: 88 iid: 2 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 120651482 120651482 NLM27927935 10.1212/WNL.0000000000003490 NLM27927935 120651482 ppf: 164 ppct: 5 formats: tig: atl: Large-scale identification of patients with cerebral aneurysms using natural language processing. aug: au: Castro, Victor M. Dligach, Dmitriy Finan, Sean Sheng Yu Can, Anil Abd-El-Barr, Muhammad Gainer, Vivian Shadick, Nancy A. Murphy, Shawn Tianxi Cai Savova, Guergana Weiss, Scott T. Du, Rose Yu, Sheng Cai, Tianxi affil: Research Information Systems and Computing, Partners Healthcare, Boston, MA. sug: subj: Natural Language Processing Cerebral Aneurysm Diagnosis Case Control Studies Female Male Aged Cerebral Aneurysm Physiopathology Pharmacokinetics Retrospective Design Middle Age Algorithms Cerebral Aneurysm Epidemiology International Classification of Diseases Center for Epidemiological Studies Depression Scale Aged: 65+ years Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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