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Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record
Open accessAbstract
BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We developed and validated EHR-based algorithms that incorporate billing codes and clinical data to identify SSc patients in the EHR. METHODS: We used a de-identified EHR with over 3 million subjects and identified 1899 potential SSc subjects with at least 1 count of the SSc ICD-9 (710.1) or ICD-10-CM (M34*) codes. We randomly selected 200 as a training set for chart review. A subject was a case if diagnosed with SSc by a rheumatologist, dermatologist, or pulmonologist. We selected the following algorithm components based on clinical knowledge and available data: SSc ICD-9 and ICD-10-CM codes, positive antinuclear antibody (ANA) (titer ≥ 1:80), and a keyword of Raynaud's phenomenon (RP). We performed both rule-based and machine learning techniques for algorithm development. Positive predictive values (PPVs), sensitivities, and F-scores (which account for PPVs and sensitivities) were calculated for the algorithms. RESULTS: PPVs were low for algorithms using only 1 count of the SSc ICD-9 code. As code counts increased, the PPVs increased. PPVs were higher for algorithms using ICD-10-CM codes versus the ICD-9 code. Adding a positive ANA and RP keyword increased the PPVs of algorithms only using ICD billing codes. Algorithms using ≥ 3 or ≥ 4 counts of the SSc ICD-9 or ICD-10-CM codes and ANA positivity had the highest PPV at 100% but a low sensitivity at 50%. The algorithm with the highest F-score of 91% was ≥ 4 counts of the ICD-9 or ICD-10-CM codes with an internally validated PPV of 90%. A machine learning method using random forests yielded an algorithm with a PPV of 84%, sensitivity of 92%, and F-score of 88%. The most important feature was RP keyword. CONCLUSIONS: Algorithms using only ICD-9 codes did not perform well to identify SSc patients. The highest performing algorithms incorporated clinical data with billing codes. EHR-based algorithms can identify SSc patients across a healthcare system, enabling researchers to examine important outcomes.
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“Jamian et al (2019) utilized EHRs to identify SSc, finding that the best algorithms combined clinical and billing codes, with top features including positive antinuclear antibody titers and presence of Raynaud’s phenomenon keyword.”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Jamian et al, 2019
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Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations
Chan, S. Y., Reddy, V. D., Myers, B. A., Thibodeaux, Q. G., Brownstone, N. D., & Liao, W. J. (2020) · Dermatology and Therapy · doi:10.1007/s13555-020-00372-0 · Cited by 285
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Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer
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Metadata
- Authors
- Jamian, L., Wheless, L., Crofford, L. J., & Barnado, A.
- Affiliations
- Vanderbilt University Medical Center (US)
- Corresponding author
- Barnado, April
- Venue
- Arthritis Research & Therapy
- Published
- 2019-12
- Volume
- 21
- Issue
- 1
- Pages
- 305-305
- ISSN
- 1478-6354 (ISSN-L)
- 1478-6362
- PMC
- PMC6937803
- OpenAlex
- W2997101676
- Cited by
- 57
- References
- 14
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 7 · 2023: 13 · 2022: 6 · 2021: 11 · 2020: 5
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Arthritis Research & Therapy (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Child Health and Human Development (NICHD 5K12HD043483-12)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (1 K08AR072757-01)
- National Center for Research Resources (UL1 RR024975)
- National Center for Advancing Translational Sciences (ULTR000445)
- Topics
- Health Sciences › Medicine › Rheumatology › Systemic Sclerosis and Related Diseases
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- systemic sclerosis, electronic health records, patient identification algorithm, ICD-10-CM codes, ICD-9 codes, billing codes, antinuclear antibodies, Raynaud's phenomenon, random forest, rule-based algorithm, rare diseases, positive predictive value, sensitivity and specificity, chart review validation, clinical data integration, health information exchange
- MeSH terms
- Machine Learning, Adult, Aged, Aged, 80 and over, Algorithms, Female, Humans, Male, Middle Aged, Scleroderma, Systemic, Sensitivity and Specificity, Reproducibility of Results, Databases, Factual, International Classification of Diseases, Electronic Health Records
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