An interactive computer simulator of the circulation for knowledge acquisition in cardio-anesthesia

Fuzzy logic and control: principal approach and potential applications in medicine

During the last few years intelligent machines appeared in nearly all technical areas, such as consumer electronics, robotics, and industrial control systems. There are for example washing machines that work very effectively, need comparably less power than in the past, and have short execution times because they adjust their washing cycles to each set of clothes and change their washing strategies as the clothes become clean. These intelligent systems are based on fuzzy control strategies, i.e., common sense rules are used to describe a system's behavior instead of complex mathematical models. We have applied this new technology to control problems as well as to reasoning problems in biomedical engineering where appropriate mathematical models could not be built due to the complexity of the problem. After a short introduction to the concepts of fuzzy logic two approaches in the field are described: a fuzzy control strategy for the pump rate adjustment of a novel total artificial heart and an intelligent alarm system based on fuzzy inference which supports the anesthetist in monitoring and evaluating the hemodynamic state of a patient undergoing cardiac surgery. These examples indicate the inherent reliability and stability of this technique in the field of complex dynamic systems. Such properties are highly significant especially in medical applications.

Knowledge-based decision support for patient monitoring in cardioanesthesia

An approach to generating 'intelligent alarms' is presented that aggregates many information items, i.e. measured vital signs, recent medications, etc., into state variables that more directly reflect the patient's physiological state. Based on these state variables the described decision support system AES-2 also provides therapy recommendations. The assessment of the state variables and the generation of therapeutic advice follow a knowledge-based approach. Aspects of uncertainty, e.g. a gradual transition between 'normal' and 'below normal', are considered applying a fuzzy set approach. Special emphasis is laid on the ergonomic design of the user interface, which is based on color graphics and finger touch input on the screen. Certain simulation techniques considerably support the design process of AES-2 as is demonstrated with a typical example from cardioanesthesia.