An expert system based on causal knowledge: validation on post-cardiosurgical patients

Abstract

A new expert system for the analysis of post-cardiosurgical patients in Intensive Care Units is described, and a preliminary validation performed. The inference engine employs a hybrid reasoning method which integrates quantitative and qualitative simulation techniques in an original manner. The long-term knowledge consists of a causal network which reproduces the main relationships between physiological quantities involved in the course after cardiac surgery. Emphasis has been given to respiratory and metabolic, as well as cardiovascular quantities both in the systemic and pulmonary circulations. Preliminary system validation has been performed on a set of 40 cardiosurgical patients, previously classified either at normal-risk (17 patients) or at high-risk (23 patients) by means of statistical classification techniques. In most cases, predictions of the expert system substantially agree with those provided by the more traditional statistical method. The system, however, is also able to furnish detailed explanations on the possible physiological causes responsible for the patient status. In particular, simulation results indicate that a reduction in the cardiac index (19 cases) and an increase in the oxygen utilization coefficient (19 cases) are the most critical alterations in the high-risk patients. The system imputes the reduced cardiac index to a rise in total systemic resistance (15 high-risk patients), a decrease in cardiac strength (2 high-risk patients) or an insufficient filling volume of the systemic circulation (4 high-risk patients). Furthermore, in 6 high-risk patients the depressed cardiac outflow occurs with a reduction in the arterial oxygen content, mainly imputable to an insufficiency of blood hemoglobin content. Finally, two examples of the complete expert system explanatory capabilities are shown with reference to a pair of high-risk patients and discussed.