Identifying Axial Spondyloarthritis in Electronic Medical Records of US Veterans

Walsh, Jessica A.; Shao, Yijun; Leng, Jianwei; He, Tao; Teng, Chia‐Chen; Redd, Doug; Zeng, Qing; Burningham, Zachary; Clegg, Daniel O.; Sauer, Brian C.

doi:10.1002/acr.23140

Cited by 24 publications

(31 citation statements)

References 19 publications

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“…The remaining 16 papers focused on processing clinical notes of different types of chronic diseases. Three studies concern diseases of the musculoskeletal system and connective tissue, in particular classification of snippets of text related to axial spondyloarthritis in the EMRs of US military veterans using NLP and SVM [94], phenotyping systemic lupus erythematosus [95], and identification of rheumatoid arthritis patients via ontology-based NLP and logistic regression [96]. In the domain of diseases of the digestive system, Chen et al [97] used natural language features from pathology reports to identify celiac disease patients, Soguero-Ruiz et al [98] used feature selection and SVMs to detect early complications after colorectal cancer, and Chang et al [99] integrated rule-based NLP on notes with ICD-9s and lab values in an algorithm to better define and risk-stratify patients with cirrhosis.…”

Section: Resultsmentioning

confidence: 99%

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

359

259

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Background Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. Objective The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and their variations as keywords to maximize coverage of the articles. Results Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision . The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods...

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Section: Resultsmentioning

confidence: 99%

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

359

259

View full text Add to dashboard Cite

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“…Machine learning approaches may create opportunities to transfer some of the burden of disease detection away from healthcare providers and patients and potentially decrease the time to diagnosis. In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of these diseases using administrative claims [40] and electronic medical record (EMR) [41–43] data. In the claims-based model, the positive predictive value in predicted patients (6.24%) was 5× higher compared with that of a clinical model developed based on ankylosing spondylitis clinical features (1.29%) [40].…”

Section: Opportunities and Potential Benefits With Machine Learning Imentioning

confidence: 99%

Application of machine learning in the diagnosis of axial spondyloarthritis

Walsh

Rozycki²,

et al. 2019

Current Opinion in Rheumatology

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Purpose of review In this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA). Recent findings In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis. With additional clinical data, machine-learning algorithms developed using electronic medical records identified patients with axSpA with 82.6–91.8% accuracy. These two algorithms have helped us understand potential opportunities and challenges associated with each data set and with different analytic approaches. Efforts to refine and validate these machine-learning models are ongoing. Summary We discuss the challenges and benefits of machine-learning models in healthcare, along with potential opportunities for its application in the field of rheumatology, particularly in the early diagnosis of axSpA and ankylosing spondylitis.

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“…Details about algorithm development were previously published 16,18 . In brief, the Full Algorithm is the most comprehensive with 3 natural language processing (NLP) models 19 20,21,22,23 .…”

Section: Methodsmentioning

confidence: 99%

Identifying Patients With Axial Spondyloarthritis in Large Datasets: Expanding Possibilities for Observational Research

et al. 2020

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Objective Observational research of axial spondyloarthritis (axSpA) is limited by a lack of methods for identifying diverse axSpA phenotypes in large datasets. AxSpA identification algorithms were previously designed to identify a broad spectrum of axSpA patients, including patients not identifiable with diagnosis codes. The study objective was to estimate the performance of axSpA identification methods in the general Veterans Affairs (VA) population. Methods A patient sample with known axSpA status (n=300) was established with chart review. For feasibility, this sample was enriched with Veterans with axSpA risk factors. Algorithm performance outcomes included sensitivities, positive predictive values (PPV), and F1 scores (an overall performance metric combining sensitivity and PPV). Performance was estimated with unweighted outcomes for the axSpA-enriched sample and inverse probability weighted (IPW) outcomes for the general VA population. These outcomes were also assessed for traditional identification methods using diagnosis codes for the ankylosing spondylitis (AS) subtype of axSpA. Results The mean age was 54.7 and 92% were male. Unweighted F1s (0.59-0.74) were higher than IPW F1s (0.48-0.65). The Full Algorithm had the best overall performance (F1IPW 0.65). The Early Algorithm was the most inclusive (sensitivityIPW 0.90, PPVIPW 0.38). The traditional method using ≥2 AS diagnosis codes from rheumatology had the highest PPV (PPVIPW 0.84, sensitivityIPW 0.34). Conclusion The axSpA identification methods demonstrated a range of performance attributes in the general VA population that may be appropriate for various types of studies. The novel identification algorithms may expand the scope of research, by enabling identification of more diverse axSpA populations.

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Identifying Axial Spondyloarthritis in Electronic Medical Records of US Veterans

Cited by 24 publications

References 19 publications

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Application of machine learning in the diagnosis of axial spondyloarthritis

Identifying Patients With Axial Spondyloarthritis in Large Datasets: Expanding Possibilities for Observational Research

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