TY - GEN
T1 - Ontology-based knowledge acquisition for neuromotor functional recovery in stroke
AU - Townsend, Christopher
AU - Huang, Jingshan
AU - Dou, Dejing
AU - Liu, Haishan
AU - He, Lei
AU - Hayes, Patrick
AU - Rudnick, Robert
AU - Shah, Hardik
AU - Fell, Dennis
AU - Liu, Wei
PY - 2010
Y1 - 2010
N2 - Hemiparesis is the most common impairment after stroke, the leading cause of adult disability in the United States. The initial severity of hemiparesis had been the strongest predictor of neuromotor functional recovery level. However, the intervention response of stroke survivors does not always correlate with their initial level of impairment. This implies the existence of other factors that may significantly affect stroke survivors' recovery process. In order to design targeting intervention therapy strategies, it is critical to consider these factors in a principled, comprehensive way so that physical rehabilitation (PR) researchers may predict which stroke survivors will respond best to therapy and subsequently, determine if a particular type of therapy is a more optimal match. Currently, such prediction is primarily a manual process and remains a challenging task to PR researchers and clinicians. We propose a computing framework based upon a domain-specific ontology. This framework aims to facilitate knowledge acquisition from existing sources via semantics-enhanced data mining (SEDM) techniques. As a result, it will assist PR researchers and clinicians in better predicting stroke survivors' neuromotor functional recovery level, and will help physical therapists customize most etTective intervention therapy plans for individual stroke survivors.
AB - Hemiparesis is the most common impairment after stroke, the leading cause of adult disability in the United States. The initial severity of hemiparesis had been the strongest predictor of neuromotor functional recovery level. However, the intervention response of stroke survivors does not always correlate with their initial level of impairment. This implies the existence of other factors that may significantly affect stroke survivors' recovery process. In order to design targeting intervention therapy strategies, it is critical to consider these factors in a principled, comprehensive way so that physical rehabilitation (PR) researchers may predict which stroke survivors will respond best to therapy and subsequently, determine if a particular type of therapy is a more optimal match. Currently, such prediction is primarily a manual process and remains a challenging task to PR researchers and clinicians. We propose a computing framework based upon a domain-specific ontology. This framework aims to facilitate knowledge acquisition from existing sources via semantics-enhanced data mining (SEDM) techniques. As a result, it will assist PR researchers and clinicians in better predicting stroke survivors' neuromotor functional recovery level, and will help physical therapists customize most etTective intervention therapy plans for individual stroke survivors.
KW - Data mining
KW - Extremity dysfunction
KW - Formal semantics
KW - Hemiparesis
KW - Intervention therapy
KW - Neuromotor functional recovery
KW - Ontology
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=79952010431&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952010431&partnerID=8YFLogxK
U2 - 10.1109/BIBMW.2010.5703839
DO - 10.1109/BIBMW.2010.5703839
M3 - Conference contribution
AN - SCOPUS:79952010431
SN - 9781424483044
T3 - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
SP - 424
EP - 429
BT - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Y2 - 18 December 2010 through 21 December 2010
ER -