Heimberg, Richard G.; Giovannetti, Tania; Drabick, Deborah A.; Conner, Bradley T.; Ellman, Lauren M.; McCloskey, Michael S. (Temple University. Libraries, 2010)
      The overall goal of the present study was to demonstrate that intolerance of uncertainty (IU) is a key feature in both generalized anxiety disorder (GAD) and obsessive-compulsive disorder (OCD). More specifically, I wanted to test certain portions of the conceptual models proposed for the study suggesting that when participants with GAD or OCD are faced with situations that tap into their idiographic concerns, they experience an increase in IU and subsequently either worry or obsessing/ritualizing. College students who met predetermined cutoff scores on study measures were assigned to analogue GAD, OCD, or control groups. The stimuli consisted of scripts that were generated to induce a sense of uncertainty in participants. It was anticipated that, when faced with material related to their idiographic concerns, the experience of uncertainty would lead them to become intolerant of the uncertain thoughts and feelings, thereby leading to increased worry, obsessing and/or ritualizing, and/or negative affect. Each script was 349 words in length and described one of 20 GAD and OCD themes commonly occurring in the literature. Participants' levels of worry, obsessing and/or ritualizing, negative affect, and IU were assessed before and after the scripts were administered. The study design included three levels of group (GAD, OCD, Control) and two levels of script (matched vs. mismatched). Half the participants in analogue GAD and OCD groups were administered scripts associated with their specific concerns (i.e., matched), and the other half were administered scripts that were mismatched. Half of the Control group was administered scripts that were assigned to the GAD matched group and the other half received scripts assigned to the OCD matched group. The study examined several different hypotheses. IU and negative affect increased from pretest to posttest assessment. However, worry and obsessing and/or ritualizing did not. Posttest IU significantly predicted worry and obsessing and/or ritualizing. However, there were no significant differences between the three groups, nor were there any significant differences as a function of matching vs. mismatching of idiographic concerns. The present study did not find any support for a hypothesized mediational role of IU in the relationship between type of script and worry, obsessing and/or ritualizing, or negative affect. Moreover, there were no significant differences between the GAD, OCD, and Control groups in worry, obsessing and/or ritualizing, negative affect, or IU. These findings did not provide support for the proposed mixed moderation-mediation model. IU was associated with worry, OC, and negative affect, but it may not be the motivational mechanism behind changes in those constructs.
    • Knowledge Discovery Through Probabilistic Models

      Obradovic, Zoran; Vucetic, Slobodan; Davey, Adam; Latecki, Longin (Temple University. Libraries, 2012)
      Probabilistic models are dominant in many research areas. To learn those models we need to find a way to determine parameters of distributions over variables which are included in the model. The main focus of my research is related to continuous variables. Thus, Gaussian distribution over variables is the most dominant factor in all models used in this document. I have been working on different and important real-life problems such as Uncertainty of Neural Network Based Aerosol Retrieval, Regression Learning with Multiple Noise Oracles and Model Predictive Control (MPC) for Sepsis Treatment, Clustering Causes of Action in Federal Courts. These problems will be discussed in the following chapters. Aerosols, small particles emanating from natural and man-made sources, along with green house gases have been recognized as very important factors in ongoing climate changes. Accurate estimation of aerosol composition and concentration is one of the main challenges in current climate research. Algorithm for prediction of aerosol designed by domain scientists does not provide quantitative information about aerosol estimation uncertainty. We deployed algorithm which uses neural networks to determine both uncertainty and the estimation of the aerosol. The uncertainty estimator has been built under an assumption that uncertainty is a function of variables used for aerosol prediction. Also, the uncertainty of predictions has been computed as the variance of the conditional distribution of targets given the input data. In regression learning, it is often difficult to obtain the true values of the label variables, while multiple sources of noisy estimates of lower quality are readily available. To address this problem, I propose a new Bayesian approach that learns a regression model from a data with noisy labels which are provided by multiple oracles. This method gives closed form solution for model parameters and it is applicable to both linear and nonlinear regression problems. Sepsis is a medical condition characterized as a systemic inflammatory response to an infection. High mortality rate (30-35%) of septic patients is usually caused by inadequate treatment. Thus, development of tools that can aid clinicians in designing optimal strategies for inflammation treatments is of utmost importance. Towards this objective I developed a data driven approach for therapy optimization where a predictive model for patients' behavior is learned directly from historical data. As such, the predictive model is incorporated into a model predictive control optimization algorithm to find optimal therapy, which will lead the patient to a healthy state. A more careful targeting of specific therapeutic strategies to more biologically homogeneous groups of patients is essential to developing effective sepsis treatment. We propose a kernel-based approach to characterize dynamics of inflammatory response in a heterogeneous population of septic patients. The method utilizes Linear State Space Control (LSSC) models to take into account dynamics of inflammatory response over time as well as the effect of therapy applied to the patient. We use a similarity measure defined on kernels of LSSC models to find homogeneous groups of patients. In addition to clustering of dynamics of inflammatory response we also explored a clustering of civil litigation from its inception by examining the content of civil complaints. We utilize spectral cluster analysis on a newly compiled federal district court dataset of causes of action in complaints to illustrate the relationship of legal claims to one another, the broader composition of lawsuits in trial courts, and the breadth of pleading in individual complaints. Our results shed light not only on the networks of legal theories in civil litigation but also on how lawsuits are classified and the strategies that plaintiffs and their attorneys employ when commencing litigation.