Learning temporal rules to forecast instability in continuously monitored patients.
Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event.
Robotics Institute, Auton Lab, Carnegie Mellon University, Pittsburgh, PA, USA mathieug@andrew.cmu.edu.
Mathieu
M
Guillame-Bert
Robotics Institute, Auton Lab, Carnegie Mellon University, Pittsburgh, PA, USA.
Artur
A
Dubrawski
Robotics Institute, Auton Lab, Carnegie Mellon University, Pittsburgh, PA, USA.
Donghan
D
Wang
Schools of Nursing and Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Marilyn
M
Hravnak
Schools of Nursing and Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Gilles
G
Clermont
Schools of Nursing and Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Michael R
MR
Pinsky
D002319:Cardiovascular System / Q000503:physiopathology*; D005544:Forecasting; D006757:Hospital Units; D006801:Humans; D000069550:Machine Learning; D008991:Monitoring, Physiologic / Q000379:methods*; D012131:Respiratory Insufficiency / Q000175:diagnosis*; D013997:Time Factors
automated rule extraction; cardiorespiratory instability; early warning system; machine learning