Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review
Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research. We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electroni...
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| Veröffentlicht in: | American journal of respiratory and critical care medicine Jg. 196; H. 4; S. 430 - 437 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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United States
American Thoracic Society
15.08.2017
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| ISSN: | 1073-449X, 1535-4970, 1535-4970 |
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| Abstract | Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.
We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs).
The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis).
After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same.
Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice. |
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| AbstractList | Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.
We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs).
The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis).
After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same.
Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice. Rationale: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research. Objectives: We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs). Methods: The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997–2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis). Measurements and Main Results: After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same. Conclusions: Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice. Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.RATIONALEDifficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs).OBJECTIVESWe evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs).The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis).METHODSThe study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis).After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same.MEASUREMENTS AND MAIN RESULTSAfter excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same.Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice.CONCLUSIONSAsthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice. Because literature on determination of asthma status by NLP does not currently exist, it is difficult to compare our study findings with others. [...]these studies focused on text searching capabilities of NLP (i.e., record level) but did not apply the text processing and classification components of NLP at a patient level. [...]our presented work is the first report for applying an NLP approach to text extraction, processing, and classification of patients for asthma status based on a predetermined criteria. [...]our study results need to be replicated in a different study setting to ascertain the accuracy of the NLP algorithm. [...]the NLP approach for asthma ascertainment is a useful tool for asthma research and care in the era of the EMR and big data because it enables largescale clinical studies and population management. |
| Author | Rolfes, Mary C. Voge, Gretchen Park, Miguel A. Bachman, Kay A. Wi, Chung-Il Sohn, Sunghwan Ryu, Euijung Kita, Hirohito Juhn, Young J. Croghan, Ivana T. Liu, Hongfang Seabright, Alicia |
| Author_xml | – sequence: 1 givenname: Chung-Il surname: Wi fullname: Wi, Chung-Il organization: Department of Pediatric and Adolescent Medicine, Asthma Epidemiology Research Unit – sequence: 2 givenname: Sunghwan surname: Sohn fullname: Sohn, Sunghwan organization: Division of Biomedical Statistics and Informatics, and – sequence: 3 givenname: Mary C. surname: Rolfes fullname: Rolfes, Mary C. organization: Asthma Epidemiology Research Unit, Mayo Medical School, Rochester, Minnesota – sequence: 4 givenname: Alicia surname: Seabright fullname: Seabright, Alicia organization: Asthma Epidemiology Research Unit – sequence: 5 givenname: Euijung surname: Ryu fullname: Ryu, Euijung organization: Division of Biomedical Statistics and Informatics, and – sequence: 6 givenname: Gretchen surname: Voge fullname: Voge, Gretchen organization: Department of Pediatric and Adolescent Medicine, Asthma Epidemiology Research Unit, Division of Neonatology, Children’s Hospitals and Clinics of Minnesota, Minneapolis, Minnesota; and – sequence: 7 givenname: Kay A. surname: Bachman fullname: Bachman, Kay A. organization: Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota – sequence: 8 givenname: Miguel A. surname: Park fullname: Park, Miguel A. organization: Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota – sequence: 9 givenname: Hirohito surname: Kita fullname: Kita, Hirohito organization: Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota – sequence: 10 givenname: Ivana T. surname: Croghan fullname: Croghan, Ivana T. organization: Department of Medicine Research, Mayo Clinic, Rochester, Minnesota – sequence: 11 givenname: Hongfang surname: Liu fullname: Liu, Hongfang organization: Division of Biomedical Statistics and Informatics, and – sequence: 12 givenname: Young J. surname: Juhn fullname: Juhn, Young J. organization: Department of Pediatric and Adolescent Medicine, Asthma Epidemiology Research Unit |
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| Snippet | Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.
We evaluated... Because literature on determination of asthma status by NLP does not currently exist, it is difficult to compare our study findings with others. [...]these... Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and... Rationale: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.... |
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| SubjectTerms | Adolescent Algorithms Allergies Asthma Asthma - epidemiology Automation Child Child, Preschool Cohort Studies Electronic health records Electronic Health Records - statistics & numerical data Epidemiology Female Health risk assessment Humans Male Medical records Minnesota - epidemiology Natural Language Processing Original Pediatrics Prevalence Primary care Reproducibility of Results Retrospective Studies Rheumatoid arthritis Risk Factors Sensitivity and Specificity Validation studies Validity |
| Title | Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review |
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