TY - JOUR
T1 - Predicting the effectiveness of hydroxyurea in individual sickle cell anemia patients
AU - Valafar, Homayoun
AU - Valafar, Faramarz
AU - Darvill, Alan
AU - Albersheim, Peter
AU - Kutlar, Abdullah
AU - Woods, Kristy F.
AU - Hardin, John
N1 - Copyright:
Copyright 2007 Elsevier B.V., All rights reserved.
PY - 2000/2
Y1 - 2000/2
N2 - 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. Copyright (C) 2000 Elsevier Science B.V.
AB - 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. Copyright (C) 2000 Elsevier Science B.V.
KW - Artificial neural networks
KW - Hydrea
KW - Hydroxyurea
KW - Pattern recognition
KW - Sickle cell anemia
KW - Variable selection
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U2 - 10.1016/S0933-3657(99)00035-4
DO - 10.1016/S0933-3657(99)00035-4
M3 - Article
C2 - 10648847
AN - SCOPUS:0033966322
SN - 0933-3657
VL - 18
SP - 133
EP - 148
JO - Artificial Intelligence In Medicine
JF - Artificial Intelligence In Medicine
IS - 2
ER -