An application of self organizing map to detection of confused blood samples

We propose a cost-aware method of detecting blood samples confused among different patients, using self organizing maps. The map consists of a cluster reacting to confused data and that reacting to non-confused data. It is completed so that the latter cluster can become larger than the former, and allows reducing drastically the number of samples wrongly judged to be retested.