Evaluation of automatically learned intelligent alarm systems

Abstract

In this contribution it is investigated whether a combination of mathematical simulation and inductive machine learning can replace the usual knowledge elicitation techniques. To test this a domain was selected for which knowledge based systems had a high performance: intelligent alarm systems. A mathematical model of a breathing circuit and ventilated patient was implemented in PSpice. Airway pressure, gas flows and CO2 concentration were simulated with this model, during normal functioning of the breathing circuit and during several mishaps, for a wide range of simulated patients. With an inductive machine learning program, classification trees were created from the simulated patient data. The classification trees described each breathing circuit mishap in terms of changes in signal feature values with respect to the normal situation and were implemented as alarm system knowledge bases. The alarm systems were tested with data measured at 17 mechanically ventilated animals. During ventilation of the animals several mishaps were introduced. For each animal, 93-100% of all mishaps could be detected correctly by the alarm systems. The false alarm rate ranged on average from one false alarm per h to one false alarm every 2.5 h. It was concluded that the suggested approach to knowledge elicitation was successful.