Prognosis Using an Isotonic Prediction Technique

Young U. Ryu (), R. Chandrasekaran () and Varghese Jacob ()
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Young U. Ryu: School of Management, The University of Texas at Dallas, Richardson, Texas 75083-0688
R. Chandrasekaran: School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, Texas 75083-0688
Varghese Jacob: School of Management, The University of Texas at Dallas, Richardson, Texas 75083-0688

Management Science, 2004, vol. 50, issue 6, 777-785

Abstract: Outcome prediction based on historical data has been of practical and theoretical interest in many disciplines. A common type of outcome prediction is binary or discrete outcome prediction, as found in medical diagnosis and firm bankruptcy prediction. The prediction problem studied in this paper is outcome time prediction, or prognosis. Prognosis in medicine refers to a prediction of probable outcome of a disease for a patient. Patient data used as the basis for disease prognosis are usually censored because some of the patients have not experienced the outcome of the disease at the time of prognosis. A mathematical-programming approach, called isotonic prediction, is developed for the purpose of such prognosis tasks. The proposed technique is different from well-known statistical survival analysis methods, such as Kaplan-Meier product-limit estimation and Cox's regression, in that it predicts individual patients' survival time frame. Two medical applications are presented to show the applicability of the proposed isotonic prediction technique.

Keywords: censored data; disease prognosis; isotonic prediction; machine learning; survival analysis; survival time prediction (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (4)

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