Early Identification of Childhood Asthma: The Role of Informatics in an Era of Electronic Health Records

Seol, Hee Yun; Sohn, Sunghwan; Liu, Hongfang; Wi, Chung Il; Ryu, Euijung; Park, Miguel A.; Juhn, Young J.

doi:10.3389/fped.2019.00113

Cited by 10 publications

(8 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, our group recently demonstrated AI-augmented phenotyping of asthma status by applying 2 existing asthma criteria successfully identified a subgroup of asthmatic children with distinctive immunological and clinical characteristics including Th2 immune response, poor asthma outcomes, and the risk of various AIMs. 103 104 105 AI-augmented phenotyping approaches can address many limitations of traditional approaches in leveraging longitudinal EHRs such as scalability, precision, accuracy, and efficiency. We refer the readers to the recent review paper on this topic, AI approaches for advancing EHR-based clinical research in allergy, asthma, and immunology.…”

Section: Discussionmentioning

confidence: 99%

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study

Kwon

Seol

et al. 2021

Allergy Asthma Immunol Res

Self Cite

View full text Add to dashboard Cite

Our prior work and the work of others have demonstrated that asthma increases the risk of a broad range of both respiratory ( e.g. , pneumonia and pertussis) and non-respiratory ( e.g. , zoster and appendicitis) infectious diseases as well as inflammatory diseases ( e.g. , celiac disease and myocardial infarction [MI]), suggesting the systemic disease nature of asthma and its impact beyond the airways. We call these conditions asthma-associated infectious and inflammatory multimorbidities (AIMs). At present, little is known about why some people with asthma are at high-risk of AIMs, and others are not, to the extent to which controlling asthma reduces the risk of AIMs and which specific therapies mitigate the risk of AIMs. These questions represent a significant knowledge gap in asthma research and unmet needs in asthma care, because there are no guidelines addressing the identification and management of AIMs. This is a systematic review on the association of asthma with the risk of AIMs and a case study to highlight that 1) AIMs are relatively under-recognized conditions, but pose major health threats to people with asthma; 2) AIMs provide insights into immunological and clinical features of asthma as a systemic inflammatory disease beyond a solely chronic airway disease; and 3) it is time to recognize AIMs as a distinctive asthma phenotype in order to advance asthma research and improve asthma care. An improved understanding of AIMs and their underlying mechanisms will bring valuable and new perspectives improving the practice, research, and public health related to asthma.

show abstract

Section: Discussionmentioning

confidence: 99%

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study

Kwon

Seol

et al. 2021

Allergy Asthma Immunol Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…To our knowledge, PAC was the only existing predetermined criteria for asthma that determines asthma status and the index date of incident asthma retrospectively based on medical records using AI algorithm at the time of our study [24][25][26]. PAC was found to have high reliability, and extensive epidemiologic work for asthma has used PAC showing the excellent construct validity in identifying known risk factors for asthma and asthma-related adverse outcomes (e.g., serious and common infections) [27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Plos Onementioning

confidence: 99%

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial

et al. 2021

Self Cite

View full text Add to dashboard Cite

Rationale Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. Objectives To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). Methods This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. Measurements Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. Main results Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374–1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2–5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3–15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82–1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. Conclusions While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians’ burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. Trial registration ClinicalTrials.gov Identifier: NCT02865967.

show abstract

“…Although there is some NLP-based research in allergy and asthma, little research based on NLP approaches has been done in atopic dermatitis or allergic rhinitis. Apart from NLP algorithms for asthma criteria and prognosis reported by our group discussed later, [20][21][22][23]37 NLP algorithms for complex concepts such as predetermined criteria for allergic disorders are significantly limited, recognizing that valuable information required for complex predetermined criteria for allergic disorders exists in a free-text format of EHRs. NLP is a useful tool for identifying a cohort with distinctive clinical characteristics in clinical conditions with significant heterogeneity such as asthma.…”

Section: Other Areasmentioning

confidence: 99%

“…As discussed earlier, as an example, we recently developed and validated NLP algorithms for 2 existing retrospective criteria for asthma: Predetermined Asthma Criteria (PAC) and Asthma Predictive Index described in Table I 20,22,23,37 and asthma prognosis. 21 Given the significant heterogeneity in determining asthma status for asthma care and research (eg, 60 different definitions in the literature) and the limited research leveraging free texts in EHRs for asthma research, our group developed NLP algorithms for PAC and Asthma Predictive Index by conducting a retrospective birth cohort study (training cohort 5 430 and testing cohort 5 500 from the 1997-2007 Mayo Clinic Birth Cohort [n 5 8525]) that used clinician's manual chart review with annotation to apply both criteria as criterion standard and asthma status by NLP algorithms as a predictor.…”

Section: Use Cases For Nlp System In Asthma Researchmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research

Juhn¹,

Liu

2020

Journal of Allergy and Clinical Immunology

Self Cite

175

View full text Add to dashboard Cite

The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. As a large amount of valuable clinical information is locked in clinical narratives, natural language processing techniques as an artificial intelligence approach have been leveraged to extract information from clinical narratives in electronic health records. This capability of natural language processing potentially enables automated chart review for identifying patients with distinctive clinical characteristics in clinical care and reduces methodological heterogeneity in defining phenotype, obscuring biological heterogeneity in research concerning allergy, asthma, and immunology. This brief review discusses the current literature on the secondary use of electronic health record data for clinical research concerning allergy, asthma, and immunology and highlights the potential, challenges, and implications of natural language processing techniques.

show abstract

Early Identification of Childhood Asthma: The Role of Informatics in an Era of Electronic Health Records

Cited by 10 publications

References 64 publications

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research

Contact Info

Product

Resources

About