TY - GEN
T1 - Acute Hypertensive Episodes Prediction
AU - Itzhak, Nevo
AU - Nagori, Aditya
AU - Lior, Edo
AU - Schvetz, Maya
AU - Lodha, Rakesh
AU - Sethi, Tavpritesh
AU - Moskovitch, Robert
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Predicting outbursts of hazardous medical conditions and its importance has arisen significantly in recent years, particularly in patients hospitalized in the Intensive Care Unit (ICU). In hospitals worldwide, patients are developing life-threatening complications, which might lead to organ dysfunctions and, if not treated properly, to death. In this study, we use patients’ longitudinal vital signs data from the ICUs, focusing on predicting Acute Hypertensive Episodes (AHE). In this study, two approaches were used for prediction: predicting continuously whether a patient will experience an AHE in a pre-defined time period ahead using an observation sliding window, or predicting whether it will generally occur during the ICU admission, given a fixed time period from the admission. Temporal abstraction was employed to transform the heterogeneous multivariate temporal data into a uniform representation of symbolic time intervals, and frequent Time Intervals Related Patterns (TIRPs), which are used as features for classification. For comparison, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are used. Our results show that using frequent temporal patterns leads to a better AHE prediction.
AB - Predicting outbursts of hazardous medical conditions and its importance has arisen significantly in recent years, particularly in patients hospitalized in the Intensive Care Unit (ICU). In hospitals worldwide, patients are developing life-threatening complications, which might lead to organ dysfunctions and, if not treated properly, to death. In this study, we use patients’ longitudinal vital signs data from the ICUs, focusing on predicting Acute Hypertensive Episodes (AHE). In this study, two approaches were used for prediction: predicting continuously whether a patient will experience an AHE in a pre-defined time period ahead using an observation sliding window, or predicting whether it will generally occur during the ICU admission, given a fixed time period from the admission. Temporal abstraction was employed to transform the heterogeneous multivariate temporal data into a uniform representation of symbolic time intervals, and frequent Time Intervals Related Patterns (TIRPs), which are used as features for classification. For comparison, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are used. Our results show that using frequent temporal patterns leads to a better AHE prediction.
KW - Acute Hypertensive Episodes
KW - Intensive care units
KW - Outcome prediction
KW - Symbolic Time Intervals
KW - Temporal patterns
UR - http://www.scopus.com/inward/record.url?scp=85092253470&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59137-3_35
DO - 10.1007/978-3-030-59137-3_35
M3 - Conference contribution
AN - SCOPUS:85092253470
SN - 9783030591366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 392
EP - 402
BT - Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
A2 - Michalowski, Martin
A2 - Moskovitch, Robert
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020
Y2 - 25 August 2020 through 28 August 2020
ER -