Mining patient data from heterogeneous sources for decision making on administration of non invasive mechanical ventilation in intensive care units

This paper addresses the problem of decision making regarding the administration of non invasive mechanical ventilation in intensive care units. The great number of factors to take into account, its heterogeneity and diverse origin make very difficult this process. In order to facilitate this task we propose the application of data mining methods to extract knowledge from the wide and complex information available. The aim is to find out the factors influencing the success/failure of NIMV and to predict the results in future patients. These methods have not been previously applied in this field in spite of the good results obtained in other medical areas. In this work a comparative study of different algorithms has been carried out using a wide spectrum of data obtained during 6 years about 389 patients that received treatment with NIMV. The results reveal that some multiclasifiers can be useful tools for helping physicians in the choice of the best action.