A Self-Organizing Map Approach for Detecting Confusion between Blood Samples
Akitsugu Ohtsuka, Naotake Kamiura, Teijiro Isokawa, Naoki Minamide, Minoru Okamoto, Noriaki Koeda, Nobuyuki Matsui · Transactions of the Society of Instrument and Control Engineers · 2005
Self-organizing map-based methods for the detection of confusion between blood test data are presented. Learning data for the self-organizing map (SOM) is generated by subtracting each element of complete blood count (CBC) data of the immediately previous patient's results from that of the current results. The neurons in the well-trained SOM are roughly divided into two clusters: one with neurons reacting to regular input data, and the other with neurons reacting to irregular input data generated by subtraction between confused CBC data. If a winner neuron belongs to the latter cluster, it is presumed that confusion has arisen between the CBC data of different patients. In addition, a genetic algorithm is adopted to eliminate redundant elements in the CBC data, which have an unfavorable influence on the judgment of confusion. Experimental results show that the proposed methods achieve high accuracy of detection even when the input data irrelevant to the learning of maps is applied to them.