Predicting the effectiveness of hydroxyurea in individual sickle cell anemia patients

Homayoun Valafar, Faramarz Valafar, Alan Darvill, Peter Albersheim, Abdullah Kutlar, Kristy F. Woods, John Hardin

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)133-148
Number of pages16
JournalArtificial Intelligence In Medicine
Volume18
Issue number2
DOIs
StatePublished - Feb 1 2000

Keywords

  • Artificial neural networks
  • Hydrea
  • Hydroxyurea
  • Pattern recognition
  • Sickle cell anemia
  • Variable selection

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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