TY - GEN
T1 - Localized supervised metric learning on temporal physiological data
AU - Sun, Jimeng
AU - Sow, Daby
AU - Hu, Jianying
AU - Ebadollahi, Shahram
PY - 2010/11/18
Y1 - 2010/11/18
N2 - Effective patient similarity assessment is important for clinical decision support. It enables the capture of past experience as manifested in the collective longitudinal medical records of patients to help clinicians assess the likely outcomes resulting from their decisions and actions. However, it is challenging to devise a patient similarity metric that is clinically relevant and semantically sound. Patient similarity is highly context sensitive: it depends on factors such as the disease, the particular stage of the disease, and co-morbidities. One way to discern the semantics in a particular context is to take advantage of physicians' expert knowledge as reflected in labels assigned to some patients. In this paper we present a method that leverages localized supervised metric learning to effectively incorporate such expert knowledge to arrive at semantically sound patient similarity measures. Experiments using data obtained from the MIMIC II database demonstrate the effectiveness of this approach.
AB - Effective patient similarity assessment is important for clinical decision support. It enables the capture of past experience as manifested in the collective longitudinal medical records of patients to help clinicians assess the likely outcomes resulting from their decisions and actions. However, it is challenging to devise a patient similarity metric that is clinically relevant and semantically sound. Patient similarity is highly context sensitive: it depends on factors such as the disease, the particular stage of the disease, and co-morbidities. One way to discern the semantics in a particular context is to take advantage of physicians' expert knowledge as reflected in labels assigned to some patients. In this paper we present a method that leverages localized supervised metric learning to effectively incorporate such expert knowledge to arrive at semantically sound patient similarity measures. Experiments using data obtained from the MIMIC II database demonstrate the effectiveness of this approach.
UR - http://www.scopus.com/inward/record.url?scp=78149476267&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149476267&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.1009
DO - 10.1109/ICPR.2010.1009
M3 - Conference contribution
AN - SCOPUS:78149476267
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 4149
EP - 4152
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -