TY - GEN
T1 - Subgrouping rare disease patients leveraging the human phenotype ontology embeddings
AU - Shen, Feichen
AU - Wen, Andrew
AU - Liu, Hongfang
N1 - Funding Information:
This work has been supported by the National Institute of Health (NIH) grant U01TR0062-1.
Funding Information:
Acknowledgment This work has been supported by the National Institute of Health (NIH) grant U01TR0062-1.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. It usually takes substantial time and long journey for rare disease patients to seek care before receiving a correct diagnosis. Making the right phenotypic characterization is the initial step to speed up such differential diagnosis at early time and the Human Phenotype Ontology (HPO) is a comprehensive knowledgebase supporting this goal. Previously, we have constructed various node embeddings for the HPO incorporating heterogeneous biomedical knowledge repositories. In this study, we applied unsupervised learning strategies over different HPO embeddings, aiming to further subgroup rare disease patients based on phenotypic characterizations.
AB - In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. It usually takes substantial time and long journey for rare disease patients to seek care before receiving a correct diagnosis. Making the right phenotypic characterization is the initial step to speed up such differential diagnosis at early time and the Human Phenotype Ontology (HPO) is a comprehensive knowledgebase supporting this goal. Previously, we have constructed various node embeddings for the HPO incorporating heterogeneous biomedical knowledge repositories. In this study, we applied unsupervised learning strategies over different HPO embeddings, aiming to further subgroup rare disease patients based on phenotypic characterizations.
KW - Human phenotype ontology embeddings
KW - Patients subgrouping
KW - Phenotypic characterization
KW - Rare disease
UR - http://www.scopus.com/inward/record.url?scp=85091161676&partnerID=8YFLogxK
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U2 - 10.1109/CBMS49503.2020.00039
DO - 10.1109/CBMS49503.2020.00039
M3 - Conference contribution
AN - SCOPUS:85091161676
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 169
EP - 172
BT - Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020
A2 - de Herrera, Alba Garcia Seco
A2 - Rodriguez Gonzalez, Alejandro
A2 - Santosh, KC
A2 - Temesgen, Zelalem
A2 - Kane, Bridget
A2 - Soda, Paolo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020
Y2 - 28 July 2020 through 30 July 2020
ER -