**Reference: **
Rutledge, G. W. Dynamic Selection of Models. Ph.D. Thesis, Stanford University, 1995.

**Abstract:** In this dissertation, I develop an approach to high-stakes,
model-based decision making under scarce computation resources,
bringing together concepts and techniques from the disciplines of
decision analysis, statistics, artificial intelligence, and
simulation. I develop and implement a method to solve a time-critical
decision problem in the domain of critical-care medicine. This method
selects models that balance the prediction accuracy and the need for
rapid action. Under a computation-time constraint, the optimal model
for a model-based control application is the model that maximizes the
tradeoff of model benefit (a measure of how accurately the model
predicts the effects of alternative control settings) and model cost
(a measure of the length of the model-induced computation delay). This
dissertation describes a real-time algorithm that selects, from a
graph of models (GoM), a model that is accurate and that is computable
within a time constraint. The dynamic-selection-of-models (DSM)
algorithm is a metalevel reasoning strategy that relies on a DSM
metric to guide the search through a GoM that is organized according
to the simplifying assumptions of the models. The DSM metric balances
an estimate of the probability that a model will achieve the required
prediction accuracy and the cost of the expected model-induced
computation delay. The DSM algorithm provides an approach to automated
reasoning about complex systems that applies at any level of
computation-resource or computation-time constraint.
The DSM algorithm solves the model-selection problem for a
ventilator-management advisor (VMA). A VMA is a computer-based monitor
for patients in the intensive-care unit (ICU); VMAs apply
patient-specific prediction models of physiology to interpret ICU data
and to predict the effects of alternative proposed
treatments. VentPlan is a prototype VMA that implements a simplified
model of physiology to monitor postoperative ICU patients; this model
is unable to make accurate predictions for patients with complex
physiologic abnormalities, such as the abnormalities that occur in
asthma or pulmonary embolus. I describe the VentSim model, a more
detailed model of cardiopulmonary physiology that makes accurate
predictions for patients with a wide range of physiologic
abnormalities. Although VentSim is too computationally complex for use
at the inner loop of a real-time VMA, alternative simplifications of
VentSim represent a range of tradeoffs of prediction accuracy and
computation complexity.
I implement the DSM algorithm in Konan, a program that selects
patient-specific models from a GoM of alternative simplifications of
the VentSim model. Konan demonstrates that the DSM algorithm selects
models that balance the competing requirements for high prediction
accuracy and for low computation complexity; these model selections
allow a VMA to make real-time decisions for the control settings of a
mechanical ventilator.

**Notes:** April.

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