Predicting the Effectiveness of Hydroxyurea in Individual Sickle Cell Anemia Patients
The study described in this paper was undertaken to develop the ability to predict the response of sickle-cell patients to hydroxyurea (HU) therapy. We analyzed the effect of HU on the values of 23 parameters of 83 patients. A Student’s t-test was used to confirm (Rodgers GP, Dover GJ, Noguchi CT, Schechter AN, Nienhuis AW. Hematologic responses of patients with sickle cell disease to treatment with hydroxyurea, N Engl J Med 1990;322;1037–44) at the 0.001 level that treatment with HU increases the proportion of fetal hemoglobin (HbF), and the average corpuscular volume (MCV) of the red blood cells. Correlation analysis failed to establish a statistically significant relationship between any of the 23 parameters and the HbF response. Linear regression analysis also failed to predict a patient’s response to HU. On the other hand, artificial neural network (ANN) pattern-recognition analysis of the 23 parameters predicts, with 86.6% accuracy, those patients that respond positively to HU and those that do not. Furthermore, we have found that the values of only 10 of the 23 parameters (listed in the body of this paper) are sufficient to train ANNs to predict which patients will respond to HU.
Artificial Intelligence in Medicine, Volume 18, Issue 2, 2000, pages 133-148.
Copyright Elsevier, 2000.
H. Valafar, Valafar F et al., (2000). Predicting the effectiveness of hydroxyurea in individual sickle cell anemia patients. Artificial Intelligence in Medicine, 18(2): 133-148. http://dx.doi.org/10.1016/S0933-3657(99)00035-4