Natural Language Processing for Asthma Ascertainment in Different Practice Settings

Wi, Chung Il; Sohn, Sunghwan; Ali, Mir H.; Krusemark, Elizabeth A.; Ryu, Euijung; Liu, Hongfang; Juhn, Young J.

doi:10.1016/j.jaip.2017.04.041

Cited by 44 publications

(31 citation statements)

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“…The performance of individual NLP algorithm determining asthma status based on comprehensive EHRs including free text was almost close to that by humans (eg, 97% sensitivity and 95% specificity for NLP-PAC) 14. We also demonstrated external validation of our NLP algorithms for these asthma criteria across different study settings despite different population, practice and EHRs systems 16 17. Thus, such capabilities of NLP using EHRs are poised to potentially address the current challenges in asthma research and care described above by applying the existing asthma criteria to cohorts of children in a consistent manner on a large scale.…”

Section: Introductionsupporting

confidence: 52%

Expert artificial intelligence-based natural language processing characterises childhood asthma

et al. 2020

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IntroductionThe lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics.MethodsUsing the 1997–2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC−/NLP-API−)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs).ResultsOf the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC−/NLP-API−. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects.ConclusionExpert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.

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Section: Introductionsupporting

confidence: 52%

Expert artificial intelligence-based natural language processing characterises childhood asthma

et al. 2020

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“…Past studies have demonstrated that NLP can be used to obtain valuable data for research that can be more accurate than ICD codes. [7,[24][25][26] This study supports these findings, identifying NLP as clearly superior for individual phenotype algorithms. As data volume and accuracy are critical for big data initiatives, it stands to reason that NLP-derived features will yield superior models for these endeavors.…”

Section: Discussionsupporting

confidence: 73%

Augmented Intelligence with Natural Language Processing Applied to Electronic Health Records is Useful for Identifying Patients with Non-Alcoholic Fatty Liver Disease at Risk for Disease Progression

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Chan

Coca

et al. 2019

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Objective: Electronic health record (EHR) systems contain structured data and unstructured documentation. Clinical insights can be derived from analyzing both but optimal methods for this have not been studied extensively. We compared various approaches to analyzing EHR data for non-alcoholic fatty liver disease (NAFLD). Materials and Methods:We compared analysis of structured and unstructured EHR data using natural language processing (NLP), free-text search, and diagnostic codes against expert adjudication as the reference standard.Results: Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP was more sensitive than ICD and text search (NLP 0.93 vs. ICD 0.28 vs. text search 0.81) with higher a F2 score (NLP 0.92 vs. ICD 0.34 vs. text search 0.81). 619 patients had suspected NAFLD documented in radiology notes not acknowledged in other forms of clinical documentation. Of these, 232 (37.5%) were found to have more advanced liver disease after a median of 1,057 days.Discussion: NLP-based approaches have superior accuracy in identifying NAFLD within the EHR compared to ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease.Conclusion: For identification of NAFLD, NLP performed better than alternative selection modalities and facilitated follow-on analysis of information flow. If accuracy can be proven to persist across clinical domains, NLP can identify patient phenotypes for biomedical research in an accurate and high-throughput manner.

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“…Another study using multiple clinical characteristics, including FEV1, had excellent agreement between a tool for asthma ascertainment and chart review. [4] Together, these tools demonstrate the range of informatics options available to improve characterization of lung disease from the EHR.…”

Section: Discussionmentioning

confidence: 99%

“…Advanced natural language processing (NLP) tools have been developed to ascertain asthma in clinical cohorts and to extract pre-and post-bronchodilator FEV1 for patients with asthma, but require evaluation of performance in patients with fixed airflow obstruction and on a national level. [3,4] These barriers to accessing PFT results hinder our ability to assess pulmonary function on the scale necessary to develop standard phenotypes of COPD outside of dedicated observational studies and clinical trials. Phenotyping physiologic COPD based upon direct measurement of FEV1 is important for patients, clinicians and researchers to advance our understanding of the clinical burden of COPD.…”

Section: Introductionmentioning

confidence: 99%

Extracting lung function measurements to enhance phenotyping of chronic obstructive pulmonary disease (COPD) in an electronic health record using automated tools

et al. 2020

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BackgroundChronic obstructive pulmonary disease (COPD) is associated with poor quality of life, hospitalization and mortality. COPD phenotype includes using pulmonary function tests to determine airflow obstruction from the forced expiratory volume in one second (FEV1):forced vital capacity. FEV1 is a commonly used value for severity but is difficult to identify in structured electronic health record (EHR) data. Data source and methodsUsing the Microsoft SQL Server's full-text search feature and string functions supporting regular-expression-like operations, we developed an automated tool to extract FEV1 values from progress notes to improve ascertainment of FEV1 in EHR in the Veterans Aging Cohort Study (VACS). ResultsThe automated tool increased quantifiable FEV1 values from 12,425 to 16,274 (24% increase in numeric FEV1). Using chart review as the reference, positive predictive value of the tool was 99% (95% Confidence interval: 98.2-100.0%) for identifying quantifiable FEV1 values and a recall value of 100%, yielding an F-measure of 0.99. The tool correctly identified FEV1 measurements in 95% of cases.

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Natural Language Processing for Asthma Ascertainment in Different Practice Settings

Cited by 44 publications

References 50 publications

Expert artificial intelligence-based natural language processing characterises childhood asthma

Expert artificial intelligence-based natural language processing characterises childhood asthma

Augmented Intelligence with Natural Language Processing Applied to Electronic Health Records is Useful for Identifying Patients with Non-Alcoholic Fatty Liver Disease at Risk for Disease Progression

Extracting lung function measurements to enhance phenotyping of chronic obstructive pulmonary disease (COPD) in an electronic health record using automated tools

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