An important and difficult prediction task in many domains,
particularly medical decision making, is that of prognosis.  Prognosis
presents a unique set of problems to a learning system when some of
the outputs are unknown.  This paper presents a new approach to
prognostic prediction, using ideas from nonparametric statistics
to fully utilize all of the available information in a neural
architecture.  Functional knowledge transfer is used to separate the
problem into a sequence of learning tasks, all of which share a common
learned internal representation.