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• W81282775 abstract "Health care personnel make predictions routinely every day. They group patients according to disorders, render prognoses of the health status of a given patients at a future point in time, or classify laboratory specimens. With the emergence of integrated hospital information systems, the potential of using computerized predictive models to support tasks like these is significant. This thesis concerns itself with problems of realizing predictive models based on knowledge of associations, e.g., findings – diagnosis associations. The theoretical context of the work contained in this thesis lies in the intersection between artificial intelligence, machine learning in particular, statistics and medicine. Although predictive model results reported in the literature often surpass the results of control groups of relevant health care professionals, few are found in clinical practice. The reasons for this are multiple and touch upon issues concerned with ethics, sociology, psychology and technology. The work presented here aims at lowering this deployment barrier by addressing some technical problems of predictive models. It also offers methodological showcases for the use of genetic algorithms as search heuristics and the use of empirical measures in construction and adaption of predictive models. This is done in the main part of this thesis, a collection of the following five papers. Minimal Approximate Hitting Sets and Rule Templates A genetic algorithm approach to multi-disorder diagnosis A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction A recalibration method for predictive models with dichotomous outcomes Effects of Case Removal in Prognostic Models for Medicine" @default.
• W81282775 created "2016-06-24" @default.
• W81282775 creator A5003639716 @default.
• W81282775 date "2000-01-01" @default.
• W81282775 modified "2023-09-25" @default.
• W81282775 title "Predictive Models in Medicine: Some Methods for Construction and Adaptation" @default.
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