| Bayesian modeling of unknown causes of events is an important and pervasive problem. However, it has received relatively little research attention. In general, an intelligent agent (or system) has only limited causal knowledge of the world. Therefore, the agent may well be experiencing the influences of causes outside its model. For example, a clinician may be seeing a patient with a virus that is new to humans; the HIV virus was at one time such an example. It is important that clinicians be able to recognize that a patient is presenting with an unknown disease. In general, intelligent agents (or systems) need to recognize under uncertainty when they are likely to be experiencing influences outside their realm of knowledge. This dissertation investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection.;The dissertation introduces a Bayesian approach that models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities, (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities and (3) partially-known diseases (e.g., a disease that has characteristics of an influenza-like illness) by using semi-informative prior probabilities. I report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population.;A key contribution of this dissertation is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in artificial intelligence in general and biomedical informatics applications in particular, where the space of known causes of outcomes of interest is seldom complete. |
