An expert system to diagnose anemia and report results directly on hematology forms

An attempt was made to create an expert system with sufficient accuracy to diagnose classes of anemia and report presumptive diagnoses directly on the hematology form. The system should simulate the processes of human experts who can reliably achieve diagnostic separability by pattern analysis. A hybrid expert system combining rule-based and artificial neural network (ANN) models was constructed to evaluate microcytic anemia in a 3-layered program using hematocrit (HCT), mean corpuscular volume (MCV), and coefficient of variation of cell distribution width (RDWcv) as inputs. These measurements are available as standard output on most hematology analyzers. Three categories of microcytic anemia were considered, iron deficiency (IDA), hemoglobinopathy (HEM), and anemia of chronic disease (ACD). A novel feature of the model is its construction and training using human expert input alone. Model construction is described in detail. The model's performance was evaluated with actual case data. It was successful in correctly classifying 96.5% of 473 documented cases of microcytic anemia and anemia of chronic disease. It thus exhibits sufficient accuracy for it to be considered for use in reporting microcytic anemia diagnoses on hematology forms.