Integration of quantitative and qualitative reasoning: an expert system for cardiosurgical patients.
In this work the possibility of building an expert system to reason on the status of post-operative cardiac patients in intensive care units is analysed. The long-term knowledge consists of causal network which describes the main relationships between hemodynamic and metabolic quantities involved in the evolution after cardiac surgery. The inference engine uses an original hybrid formalism, which integrates numerical simulation and qualitative methods. If available, the numerical values of quantities and their exact mathematical relationships are employed; otherwise, the inference engine reasons by using a discrete qualitative representation of quantities. Simulations performed using real data indicate that integration of quantitative and qualitative methods reduces the number of diagnostic scenarios compatible with patient data, and constitutes a valid tool for reasoning about physiological disorders in terms of deep causal knowledge.
Ursino
M
M
Department of Electronics and Automatics, University of Ancona, Italy.
Artioli
E
E
Avanzolini
G
G
Potuto
V
V
D016280:Atrial Function, Right; D001775:Blood Circulation; D001794:Blood Pressure; D001810:Blood Volume; D002245:Carbon Dioxide / Q000378:metabolism; D002302:Cardiac Output; D006348:Cardiac Surgical Procedures; D003198:Computer Simulation; D003422:Critical Care; D005103:Expert Systems; D006454:Hemoglobins / Q000032:analysis; D006801:Humans; D016247:Information Storage and Retrieval; D016571:Neural Networks, Computer; D010100:Oxygen / Q000097:blood; D010101:Oxygen Consumption / Q000502:physiology; D011182:Postoperative Care; D012985:Software Design; D013813:Therapy, Computer-Assisted; D014655:Vascular Resistance; D014664:Vasodilation
D006454:Hemoglobins; D002245:Carbon Dioxide; D010100:Oxygen