Feedback was documented in the BICS system. These more specific alerts can be sent directly to physicians. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation. As a specific example it is considered to con-struct a sequential decision procedure to determine if a liver transplant patient is a high risk patient or not. The advantages of the procedure are demonstrated by a surgical problem of risk predic-tion 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 sensitiv-ity 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 continu-ous clinical test scores obtained in a time sequence. Two sets of scores derived form linear discriminant analysis and artificial neural networks are compared.