TY - GEN
T1 - A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records
AU - Gustafson, Erin
AU - Pacheco, Jennifer
AU - Wehbe, Firas
AU - Silverberg, Jonathan
AU - Thompson, William
N1 - Funding Information:
ACKNOWLEDGMENT This project is part of the eMERGE Network funded by the NHGRI, grant number U01HG8673. This project was also made possible with support from the Agency for Healthcare Research and Quality (AHRQ), grant number K12HS023001, and the Dermatology Foundation. We thank the members of the eMERGE team at Northwestern University for useful discussion at various stages of this project.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/8
Y1 - 2017/9/8
N2 - 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.
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.
KW - Electronic health records
KW - Machine learning
KW - Natural language processing
KW - Phenotyping
UR - http://www.scopus.com/inward/record.url?scp=85032365565&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032365565&partnerID=8YFLogxK
U2 - 10.1109/ICHI.2017.31
DO - 10.1109/ICHI.2017.31
M3 - Conference contribution
C2 - 29104964
AN - SCOPUS:85032365565
T3 - Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
SP - 83
EP - 90
BT - Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
A2 - Cummins, Mollie
A2 - Facelli, Julio
A2 - Meixner, Gerrit
A2 - Giraud-Carrier, Christophe
A2 - Nakajima, Hiroshi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Healthcare Informatics, ICHI 2017
Y2 - 23 August 2017 through 26 August 2017
ER -