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

Erin Gustafson, Jennifer Pacheco, Firas Wehbe, Jonathan I Silverberg, William Karl Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
EditorsMollie Cummins, Julio Facelli, Gerrit Meixner, Christophe Giraud-Carrier, Hiroshi Nakajima
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages83-90
Number of pages8
ISBN (Electronic)9781509048816
DOIs
StatePublished - Sep 8 2017
Event5th IEEE International Conference on Healthcare Informatics, ICHI 2017 - Park City, United States
Duration: Aug 23 2017Aug 26 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017

Other

Other5th IEEE International Conference on Healthcare Informatics, ICHI 2017
CountryUnited States
CityPark City
Period8/23/178/26/17

Fingerprint

Electronic Health Records
Atopic Dermatitis
Natural Language Processing
Genome-Wide Association Study
Logistic Models
Phenotype
Machine Learning

Keywords

  • Electronic health records
  • Machine learning
  • Natural language processing
  • Phenotyping

ASJC Scopus subject areas

  • Health Informatics

Cite this

Gustafson, E., Pacheco, J., Wehbe, F., Silverberg, J. I., & Thompson, W. K. (2017). A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records. In M. Cummins, J. Facelli, G. Meixner, C. Giraud-Carrier, & H. Nakajima (Eds.), Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017 (pp. 83-90). [8031135] (Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2017.31
Gustafson, Erin ; Pacheco, Jennifer ; Wehbe, Firas ; Silverberg, Jonathan I ; Thompson, William Karl. / A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records. Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. editor / Mollie Cummins ; Julio Facelli ; Gerrit Meixner ; Christophe Giraud-Carrier ; Hiroshi Nakajima. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 83-90 (Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017).
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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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Gustafson, E, Pacheco, J, Wehbe, F, Silverberg, JI & Thompson, WK 2017, A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records. in M Cummins, J Facelli, G Meixner, C Giraud-Carrier & H Nakajima (eds), Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017., 8031135, Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 83-90, 5th IEEE International Conference on Healthcare Informatics, ICHI 2017, Park City, United States, 8/23/17. https://doi.org/10.1109/ICHI.2017.31

A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records. / Gustafson, Erin; Pacheco, Jennifer; Wehbe, Firas; Silverberg, Jonathan I; Thompson, William Karl.

Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. ed. / Mollie Cummins; Julio Facelli; Gerrit Meixner; Christophe Giraud-Carrier; Hiroshi Nakajima. Institute of Electrical and Electronics Engineers Inc., 2017. p. 83-90 8031135 (Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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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Gustafson E, Pacheco J, Wehbe F, Silverberg JI, Thompson WK. A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records. In Cummins M, Facelli J, Meixner G, Giraud-Carrier C, Nakajima H, editors, Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 83-90. 8031135. (Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017). https://doi.org/10.1109/ICHI.2017.31