TY - GEN
T1 - A system for mining temporal physiological data streams for advanced prognostic decision support
AU - Sun, Jimeng
AU - Sow, Daby
AU - Hu, Jianying
AU - Ebadollahi, Shahram
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - We present a mining system that can predict the future health status of the patient using the temporal trajectories of health status of a set of similar patients. The main novelties of this system are its use of stream processing technology for handling the incoming physiological time series data and incorporating domain knowledge in learning the similarity metric between patients represented by their temporal data. The proposed approach and system were tested using the MIMIC II database, which consists of physiological waveforms, and accompanying clinical data obtained for ICU patients. The study was carried out on 1500 patients from this database. In the experiments we report the efficiency and throughput of the stream processing unit for feature extraction, the effectiveness of the supervised similarity measure both in the context of classification and retrieval tasks compared to unsupervised approaches, and the accuracy of the temporal projections of the patient data.
AB - We present a mining system that can predict the future health status of the patient using the temporal trajectories of health status of a set of similar patients. The main novelties of this system are its use of stream processing technology for handling the incoming physiological time series data and incorporating domain knowledge in learning the similarity metric between patients represented by their temporal data. The proposed approach and system were tested using the MIMIC II database, which consists of physiological waveforms, and accompanying clinical data obtained for ICU patients. The study was carried out on 1500 patients from this database. In the experiments we report the efficiency and throughput of the stream processing unit for feature extraction, the effectiveness of the supervised similarity measure both in the context of classification and retrieval tasks compared to unsupervised approaches, and the accuracy of the temporal projections of the patient data.
KW - Patient similarity
KW - Physiological streams
UR - http://www.scopus.com/inward/record.url?scp=79951734069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951734069&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2010.102
DO - 10.1109/ICDM.2010.102
M3 - Conference contribution
AN - SCOPUS:79951734069
SN - 9780769542560
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1061
EP - 1066
BT - Proceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
T2 - 10th IEEE International Conference on Data Mining, ICDM 2010
Y2 - 14 December 2010 through 17 December 2010
ER -