Event discovery in medical time-series data.
Vast amounts of clinical information are generated daily on patients in the health care setting. Increasingly, this information is collected and stored for its potential utility in advancing health care. Knowledge-based systems, for example, might be able to apply rules to the collected data to determine whether a patient has a certain condition. Often, however, the underlying knowledge needed to write such rules is not well understood. How could these clinical data be useful then? Use of machine learning is one answer. We present a pipeline for discovering the knowledge needed for event detection in medical time-series data. We demonstrate how this process can be applied in the development of intelligent patient monitoring for the intensive care unit (ICU). Specifically, we develop a system for detecting Otrue alarmO situations in the ICU, where currently as many as 86% of bedside monitor alarms are false.
Tsien
C L
CL
Massachusetts Institute of Technology, Laboratory for Computer Science, Cambridge, MA, USA.
D001185:Artificial Intelligence; D001794:Blood Pressure; D003663:Decision Trees; D006801:Humans; D006973:Hypertension / Q000175:diagnosis*; D007362:Intensive Care Units; D016014:Linear Models; D008991:Monitoring, Physiologic; D016571:Neural Networks, Computer; D019095:Point-of-Care Systems; D012372:ROC Curve; D013995:Time