Agitation and pain assessment using digital imaging.
Pain assessment in patients who are unable to verbally communicate with medical staff is a challenging problem in patient critical care. The fundamental limitations in sedation and pain assessment in the intensive care unit (ICU) stem from subjective assessment criteria, rather than quantifiable, measurable data for ICU sedation and analgesia. This often results in poor quality and inconsistent treatment of patient agitation and pain from nurse to nurse. Recent advancements in pattern recognition techniques using a relevance vector machine algorithm can assist medical staff in assessing sedation and pain by constantly monitoring the patient and providing the clinician with quantifiable data for ICU sedation. In this paper, we show that the pain intensity assessment given by a computer classifier has a strong correlation with the pain intensity assessed by expert and non-expert human examiners.
Gholami
Behnood
B
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0150, USA. behnood@gatech.edu
Haddad
Wassim M
WM
Tannenbaum
Allen R
AR
D000465:Algorithms; D001930:Brain Injuries / Q000503:physiopathology; D005149:Facial Expression; D006801:Humans; D006993:Hypnotics and Sedatives / Q000627:therapeutic use; D007223:Infant; D007362:Intensive Care Units; D016011:Normal Distribution; D010146:Pain / Q000145:classification / Q000188:drug therapy / Q000503:physiopathology; D010147:Pain Measurement / Q000379:methods*; D010149:Postoperative Pain / Q000175:diagnosis / Q000503:physiopathology / Q000517:prevention & control; D010363:Pattern Recognition, Automated / Q000379:methods; D011595:Psychomotor Agitation / Q000145:classification / Q000188:drug therapy / Q000503:physiopathology*
D006993:Hypnotics and Sedatives