Monitoring and Therapy Planning without Effective Data Validation are Ineffective

Systems for monitoring and therapy planning, which receive their data from computer-based patient records and on-line monitoring equipment, require reliable data. Reasoning on faulty data can cause unexplainable and life-threatening conclusions. Effective and efficient data validation methods are needed to arrive at reliable conclusions. We distinguished four categories of data validation and repair based on their underlying temporal ontologies: time-point-, time-interval-, trend-based, and time-independent validation and repair. Observing single measurements is not e ective to arrive at trustable data. Therefore we take into account the behavior of parameters in the past as well as knowledge derived from domain experts. Examples from VIE-VENT, a knowledge-based monitoring and therapy-planning system for artificially-ventilated newborns, demonstrate the applicability of these methods.