TY - GEN
T1 - Hypotensive episode prediction in icus via observation window splitting
AU - Tsur, Elad
AU - Last, Mark
AU - Garcia, Victor F.
AU - Udassin, Raphael
AU - Klein, Moti
AU - Brotfain, Evgeni
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Hypotension, defined as dangerously low blood pressure, is a significant risk factor in intensive care units (ICUs), which requires a prompt therapeutic intervention. The goal of our research is to predict an impending Hypotensive Episode (HE) by time series analysis of continuously monitored physiological vital signs. Our prognostic model is based on the last Observation Window (OW) at the prediction time. Existing clinical episode prediction studies used a single OW of 5–120 min to extract predictive features, with no significant improvement reported when longer OWs were used. In this work we have developed the In-Window Segmentation (InWiSe) method for time series prediction, which splits a single OW into several sub-windows of equal size. The resulting feature set combines the features extracted from each observation sub-window and then this combined set is used by the Extreme Gradient Boosting (XGBoost) binary classifier to produce an episode prediction model. We evaluate the proposed approach on three retrospective ICU datasets (extracted from MIMIC II, Soroka and Hadassah databases) using cross-validation on each dataset separately, as well as by cross-dataset validation. The results show that InWiSe is superior to existing methods in terms of the area under the ROC curve (AUC).
AB - Hypotension, defined as dangerously low blood pressure, is a significant risk factor in intensive care units (ICUs), which requires a prompt therapeutic intervention. The goal of our research is to predict an impending Hypotensive Episode (HE) by time series analysis of continuously monitored physiological vital signs. Our prognostic model is based on the last Observation Window (OW) at the prediction time. Existing clinical episode prediction studies used a single OW of 5–120 min to extract predictive features, with no significant improvement reported when longer OWs were used. In this work we have developed the In-Window Segmentation (InWiSe) method for time series prediction, which splits a single OW into several sub-windows of equal size. The resulting feature set combines the features extracted from each observation sub-window and then this combined set is used by the Extreme Gradient Boosting (XGBoost) binary classifier to produce an episode prediction model. We evaluate the proposed approach on three retrospective ICU datasets (extracted from MIMIC II, Soroka and Hadassah databases) using cross-validation on each dataset separately, as well as by cross-dataset validation. The results show that InWiSe is superior to existing methods in terms of the area under the ROC curve (AUC).
KW - Clinical episode prediction
KW - Feature extraction
KW - Intensive care
KW - Patient monitoring
KW - Time series analysis
UR - http://www.scopus.com/inward/record.url?scp=85061159109&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-10997-4_29
DO - 10.1007/978-3-030-10997-4_29
M3 - Conference contribution
AN - SCOPUS:85061159109
SN - 9783030109967
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 472
EP - 487
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
A2 - Brefeld, Ulf
A2 - Marascu, Alice
A2 - Pinelli, Fabio
A2 - Curry, Edward
A2 - MacNamee, Brian
A2 - Hurley, Neil
A2 - Daly, Elizabeth
A2 - Berlingerio, Michele
PB - Springer Verlag
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
Y2 - 10 September 2018 through 14 September 2018
ER -