TY - GEN
T1 - Genetic testing knowledge base (GTKB) towards individualized genetic test recommendation - An experimental study
AU - Zhu, Qian
AU - Liu, Hongfang
AU - Chute, Christopher G.
AU - Ferber, Matthew
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/29
Y1 - 2014/12/29
N2 - The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clinical practice poses barriers to effective reliance on advanced genetic technologies to improve quality of healthcare. A promising solution to encourage and assist physicians to incorporate genetic tests in their clinical practice is an intelligent genetic test recommendation system for 1) providing a comprehensive view of genetic tests as education resources; 2) recommending the most appropriate genetic tests to patients based on clinical evidence. In this paper, we introduce a genetic testing knowledge base, called GTKB, which was designed to support further individualized genetic test recommendation. More specifically, we extracted clinical characteristics identified from Electronic Health Records (EHRs) that have been used as phenotypic information for linked archived biological material to accelerate research in individualized medicine, and well-documented public genetic testing resources including Genetic Testing Registry (GTR) and published genetic testing guidelines (GTG) to construct a genetic test orientated knowledge base, ultimately supporting genetic test recommendation. An experimental study for 'wilson disease mutation screen test' has been conducted to demonstrate the identification of salient clinical characteristics and the process of incorporating EHR derived phenotypes into the GTKB construction.
AB - The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clinical practice poses barriers to effective reliance on advanced genetic technologies to improve quality of healthcare. A promising solution to encourage and assist physicians to incorporate genetic tests in their clinical practice is an intelligent genetic test recommendation system for 1) providing a comprehensive view of genetic tests as education resources; 2) recommending the most appropriate genetic tests to patients based on clinical evidence. In this paper, we introduce a genetic testing knowledge base, called GTKB, which was designed to support further individualized genetic test recommendation. More specifically, we extracted clinical characteristics identified from Electronic Health Records (EHRs) that have been used as phenotypic information for linked archived biological material to accelerate research in individualized medicine, and well-documented public genetic testing resources including Genetic Testing Registry (GTR) and published genetic testing guidelines (GTG) to construct a genetic test orientated knowledge base, ultimately supporting genetic test recommendation. An experimental study for 'wilson disease mutation screen test' has been conducted to demonstrate the identification of salient clinical characteristics and the process of incorporating EHR derived phenotypes into the GTKB construction.
KW - Genetic test
KW - Individualized medicine
KW - electronic health record
KW - wilson disease
UR - http://www.scopus.com/inward/record.url?scp=84922783752&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84922783752&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2014.6999223
DO - 10.1109/BIBM.2014.6999223
M3 - Conference contribution
AN - SCOPUS:84922783752
T3 - Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
SP - 574
EP - 577
BT - Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
A2 - Zheng, Huiru
A2 - Hu, Xiaohua Tony
A2 - Berrar, Daniel
A2 - Wang, Yadong
A2 - Dubitzky, Werner
A2 - Hao, Jin-Kao
A2 - Cho, Kwang-Hyun
A2 - Gilbert, David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
Y2 - 2 November 2014 through 5 November 2014
ER -