Knowledge base design for decision support in respirator therapy.

Abstract

A knowledge base is built for decision support applied to respirator therapy (the KUSIVAR project). The knowledge representation is object-oriented using frames to store multiple forms of knowledge: variable descriptions, transformation tables, rules and mathematical models. The system is data-driven, generating and displaying advice automatically triggered by changes in data from the respirator and the patient. The inferenceing mechanism is forward-chaining i.e. a rule is evaluated as soon as it's condition is satisfied. Temporal aspects of the reasoning are represented by a number of mechanisms, among others limited validity times for data, trend analysis and mathematical models. The knowledge base is organized according to disease groups and decision situation which simplifies knowledge acquisition and improves response times since it enables the system to focus on a limited set of rules in each situation. To test the feasibility of the system design a prototype has been built using Knowledge Engineering Environment (KEE) from Intellicorp on an Explorer workstation from Unisys. The production system, which is interfaced to a Siemens Elema Servo Ventilator 900C, is currently being implemented under the Microsoft Windows multitasking environment on a microcomputer based on an Intel 80386 processor.