published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

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Artificial Intelligence Lab, Department of MIS, University of Arizona / This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient-related demographic variables (age, weight, gender, and other underlying illness), the individualâ s dosing regimens (dose and dosing interval), time of blood drawn, and the resulting tobramycin concentration. Neural networks were trained with this information to capture the relationships between the plasma tobramycin levels and the following factors: patient-related demographic factors, dosing regimens, and time of blood drawn. The data were also analyzed using a standard population pharmacokinetic modeling program, NONMEM. The observed vs predicted concentration relationships obtained from the neural network approach were similar to those from NONMEM. The residuals of the predictions from neural network analyses showed a positive correlation with that from NONMEM. Average absolute errors were 33.9 and 37.3% for neural networks and 39.9% for NONMEM. Average prediction errors were found to be 2.59 and -5.01% for neural networks and 17.7% for NONMEM. We concluded that neural networks were capable of capturing the relationships between plasma drug levels and patient-related prognostic factors from routinely collected sparse withinpatient pharmacokinetic data. Neural networks can therefore be considered to have potential to become a useful analytical tool for population pharmacokinetic data analysis.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customersâ preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona / Fear about identity verification reached new heights since the terrorist attacks on Sept. 11, 2001, with national security issues related to detecting identity deception attracting more interest than ever before. Identity deception is an intentional falsification of identity in order to deter investigations. Conventional investigation methods run into difficulty when dealing with criminals who use deceptive or fraudulent identities, as the FBI discovered when trying to determine the true identities of 19 hijackers involved in the attacks. Besides its use in post-event investigation, the ability to validate identity can also be used as a tool to prevent future tragedies. Here, we focus on uncovering patterns of criminal identity deception based on actual criminal records and suggest an algorithmic approach to revealing deceptive identities.