Evolutionary computation in the identification of risk factors. Case of TRALI

This paper presents the use of an evolutionary algorithm hybridized with the concepts of testor and typical testor in determining factors associated with transfusion related acute lung injury (TRALI). Although nowadays many cases of this syndrome remain ignored or misdiagnosed, this is the leading cause of morbidity and mortality related to transfusion in the United States. This research was conducted with data from 174 cases collected in the Centenary Hospital Miguel Hidalgo in the city of Aguascalientes, Mexico, in the period 2007 to 2010. The proposed algorithm works with information from the model known as ''two hits'', in which the first hit is the original disease and the second corresponds to the blood transfusion. This algorithm was strengthened with mechanisms that let it do an efficient search in the whole solution space. In addition to the calculation of the informational weight, the algorithm also establishes the cutoff point that determines the variables that impact the most. From the results given by the algorithm and the cutoff proposed by the medical staff, a strategy for the treatment of patients that should be transfused was proposed. This study confirmed some of the risk factors previously reported in the literature, and also made an interesting discovery.