Application of an artificial intelligence program to therapy of high-risk surgical patients.
We developed an artificial intelligence program from a large computerized database of hemodynamic and oxygen transport measurements together with prior studies defining survivors' values, outcome predictors, and a branched-chain decision tree. The artificial intelligence program was then tested on the data of 100 survivors and 100 nonsurvivors not used for the development of the program or other analyses. Using the predictor as a surrogate outcome measure, the therapy recommended by the program improved the predicted outcome 3.16% per therapeutic intervention while the actual therapy given increased outcome 1.86% in surviving patients; the artificial intelligence-recommended therapy improved outcome 7.9% in nonsurvivors, while the actual therapy given increased predicted outcome -0.29% in nonsurvivors (p < .05). There were fewer patients whose predicted outcome decreased after recommended treatment (14%) than after the actual therapy given (37%). Review of therapy recommended by the program did not reveal instances of inappropriate or potentially harmful recommendations.
Patil
R S
RS
Information Science Institute, University of Southern California School of Medicine, Los Angeles, USA.
Adibi
J
J
Shoemaker
W C
WC
D001185:Artificial Intelligence; D003663:Decision Trees; D006439:Hemodynamics; D006801:Humans; D008991:Monitoring, Physiologic; D011183:Postoperative Complications / Q000175:diagnosis / Q000628:therapy*; D015656:Respiratory Mechanics; D012307:Risk Factors