Automated chart review for asthma cohort identification using natural language processing: an exploratory study.
Ann Allergy Asthma Immunol
; 111(5): 364-9, 2013 Nov.
Article
em En
| MEDLINE
| ID: mdl-24125142
ABSTRACT
BACKGROUND:
A significant proportion of children with asthma have delayed diagnosis of asthma by health care providers. Manual chart review according to established criteria is more accurate than directly using diagnosis codes, which tend to under-identify asthmatics, but chart reviews are more costly and less timely.OBJECTIVE:
To evaluate the accuracy of a computational approach to asthma ascertainment, characterizing its utility and feasibility toward large-scale deployment in electronic medical records.METHODS:
A natural language processing (NLP) system was developed for extracting predetermined criteria for asthma from unstructured text in electronic medical records and then inferring asthma status based on these criteria. Using manual chart reviews as a gold standard, asthma status (yes vs no) and identification date (first date of a "yes" asthma status) were determined by the NLP system.RESULTS:
Patients were a group of children (n = 112, 84% Caucasian, 49% girls) younger than 4 years (mean 2.0 years, standard deviation 1.03 years) who participated in previous studies. The NLP approach to asthma ascertainment showed sensitivity, specificity, positive predictive value, negative predictive value, and median delay in diagnosis of 84.6%, 96.5%, 88.0%, 95.4%, and 0 months, respectively; this compared favorably with diagnosis codes, at 30.8%, 93.2%, 57.1%, 82.2%, and 2.3 months, respectively.CONCLUSION:
Automated asthma ascertainment from electronic medical records using NLP is feasible and more accurate than traditional approaches such as diagnosis codes. Considering the difficulty of labor-intensive manual record review, NLP approaches for asthma ascertainment should be considered for improving clinical care and research, especially in large-scale efforts.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Asma
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Processamento de Linguagem Natural
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Processamento Eletrônico de Dados
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Sistemas Computadorizados de Registros Médicos
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Guideline
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Child, preschool
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Female
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Humans
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Male
Idioma:
En
Revista:
Ann Allergy Asthma Immunol
Assunto da revista:
ALERGIA E IMUNOLOGIA
Ano de publicação:
2013
Tipo de documento:
Article