Building intelligent alarm systems by combining mathematical models and inductive machine learning techniques Part 2—Sensitivity analysis

• Published in: International journal of bio-medical computing Vol. 42; no. 3; pp. 165 - 179
• Main Authors: Müller, B, Hasman, A., Blom, J.A.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01-08-1996
• Subjects: Airway Resistance - physiology; Anesthesia; Animals; Artificial Intelligence; Compliance; Computer Simulation; Decision Trees; Humans; Inductive machine learning; Intelligent alarm systems; Lung - physiology; Mathematical modeling; Mechanical ventilation; Models, Theoretical; Monitoring, Physiologic - instrumentation; Respiration - physiology; Respiration, Artificial - instrumentation; Respiratory mechanics; Sheep; Thorax - physiology; Tidal Volume; Inductive machine learning; Intelligent alarm systems; Anesthesia; Respiratory mechanics; Mathematical modeling; Mechanical ventilation
• ISSN: 0020-7101
• DOI: 10.1016/0020-7101(96)01210-X

In an earlier study an approach was described to generate intelligent alarm systems for monitoring ventilation of patients via mathematical simulation and machine learning. However, ventilator settings were not varied. In this study we investigated whether an alarm system could be created with which a satisfactory classification performance could be obtained under a wide variety of ventilator settings, by varying inspiratory to expiratory time (I:E) ratio, tidal volume and respiratory rate. In a first experiment three patient data sets were modeled, each with a different I:E ratio. A part of each data set was used to construct an alarm system for each I:E ratio. The remaining part was used to test the performance of the alarm systems. The three training sets were also combined to construct one alarm system, which was tested with the three test sets. Finally, all alarm systems were tested with data generated by a patient simulator. Similar experiments were performed for the tidal volume and the respiratory rate. It was concluded that an optimally functioning alarm system should contain a library of rule sets, one for each set of ventilator settings. A second best alternative is to take all possible settings into consideration when constructing the training set. Classification performance of the trees that were trained with multiple ventilator settings ranged from 98 to 100% for all test sets. When tested with the independent patient simulator data the classification performance of these trees ranged from 80 to 100%.