An optimization model for sequential decision-making applied to risk prediction after liver resection and transplantation.
The paper demonstrates a sequential decision procedure, which allows for optimal cost-effective or early decision-making while maintaining given error constraints. As a specific example the construction of a sequential decision procedure to determine if a patient was a high risk patient or not is used. The advantages of the procedure are demonstrated by a surgical problem of risk prediction from a clinical study on liver resection and transplantation. Data are available pre, peri- and postoperative, and form the basis of three clinical scores. The quality of the procedure is measured in terms of sensitivity and specificity, and the procedure will be optimized in a way, that a priori given error constraints will be maintained. A decision theoretic model is introduced and a robust procedure is developed. The approach is feasible for any fixed number of continuous clinical test scores obtained in a time sequence. Two sets of scores derived form linear discriminant analysis and artificial neural networks are compared.
Tusch
G
G
Department of Abdominal and Transplantation Surgery, Medical School Hanover, Germany.
D006528:Carcinoma, Hepatocellular / Q000175:diagnosis / Q000401:mortality / Q000601:surgery*; D003657:Decision Making; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D006801:Humans; D008099:Liver / Q000601:surgery*; D008113:Liver Neoplasms / Q000175:diagnosis / Q000401:mortality / Q000601:surgery*; D016031:Liver Transplantation / Q000401:mortality; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods*