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Cited by 65Machine Learning in HealthcareArtificial Intelligence in HealthcareDermatology and Skin Diseases

A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records

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Gustafson, Erin, Pacheco, Jennifer, Wehbe, Firas, Silverberg, Jonathan, Thompson, William·2017·2017 IEEE International Conference on Healthcare Informatics (ICHI)
DOI 10.1109/ichi.2017.31PMC PMC5664951
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Abstract

The current work aims to identify patients with atopic dermatitis for inclusion in genome-wide association studies (GWAS). Here we describe a machine learning-based phenotype algorithm. Using the electronic health record (EHR), we combined coded information with information extracted from encounter notes as features in a lasso logistic regression. Our algorithm achieves high positive predictive value (PPV) and sensitivity, improving on previous algorithms with low sensitivity. These results demonstrate the utility of natural language processing (NLP) and machine learning for EHR-based phenotyping.

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  • Models trained on EHR data have successfully classified AD.

    “Models trained on EHR data, which include least absolute shrinkage and selection operator (LASSO) logistic regression and natural language processing or transformer-based patient representation with XGBoost model, have successfully classified AD (Gustafson et al, 2017; Wang et al, 2024b).”

    From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Gustafson et al, 2017

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References 24 · Cited by 65

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Authors
Gustafson, E., Pacheco, J., Wehbe, F., Silverberg, J., & Thompson, W.
Affiliations
Northwestern University (US)
ORCID
Venue
2017 IEEE International Conference on Healthcare Informatics (ICHI)
Publisher
IEEE
Published
2017-08
Volume
2017
Pages
83-90
DOI
10.1109/ichi.2017.31
PMC
PMC5664951
OpenAlex
W2755599975
Cited by
65
References
24
Citations by year
2026: 3 · 2025: 7 · 2024: 7 · 2023: 14 · 2022: 11 · 2021: 6 · 2020: 10 · 2019: 3 · 2018: 3 · 2017: 1
FWCI
2.12
Type
conference paper
Language
en
Open access
Locations
  • IEEE International Conference on Healthcare Informatics (ICHI) (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Northwestern University
  • National Institutes of Health (UL1TR001422)
  • National Human Genome Research Institute (U01HG008673)
  • National Center for Advancing Translational Sciences
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
Keywords
atopic dermatitis, electronic health records, phenotypic identification, natural language processing, LASSO logistic regression, genome-wide association study

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