Prognosis using an isotonic prediction technique

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.