Individualizing generic decision models using assessments as evidence.
Complex decision models in expert systems often depend upon a number of utilities and subjective probabilities for an individual. Although these values can be estimated for entire populations or demographic subgroups, a model should be customized to the individual's specific parameter values. This process can be onerous and inefficient for practical decisions. We propose an interactive approach for incrementally improving our knowledge about a specific individual's parameter values, including utilities and probabilities, given a decision model and a prior joint probability distribution over the parameter values. We define the concept of value of elicitation and use it to determine dynamically the next most informative elicitation for a given individual. We evaluated the approach using an example model and demonstrate that we can improve the decision quality by focusing on those parameter values most material to the decision.
Scott
George C
GC
Department of Medicine, University of California, San Diego, CA 92103, USA. gcscott@ucsd.edu
Shachter
Ross D
RD
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D019317:Evidence-Based Medicine / Q000379:methods*; D017063:Outcome Assessment, Health Care / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D013813:Therapy, Computer-Assisted / Q000379:methods*