Patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools

Abstract

Patient monitoring by automated data collection has created new challenges for health care professionals in their efforts to extract useful information from raw data. New online monitoring devices may generate large amounts of data that must be interpreted quickly and accurately. The use of statistical methods and artificial intelligence tools to summarize and interpret high frequency physiologic data such as the electrocardiogram (EKG) are investigated. The development of a methodology and its associated tools for real-time patient data monitoring and diagnosis was accomplished by using the commercial programming environments MATLAB and G2, a real-time knowledge-based system (KBS) development shell. A KBS was developed that incorporates various statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis, and to report these findings to the physician.