Browsing by Subject "Computational model"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Success and failure in dynamic decision environments: understanding treatment strategies for patients with a chronic disease.
    (2010-05) Ramsey, Gregory W.
    This dissertation proposes and tests a theory explaining how people make decisions to achieve a goal in a specific task environment. The theory is represented as a computational model and implemented as a computer program. The task studied was primary care physicians treating patients with type 2 diabetes. Some physicians succeed in achieving evidence-based goals, but many fail. In a previously conducted experiment 19 physicians treated 3 simulated patients with type 2 diabetes, this was the dataset used for modeling and testing (O'Connor et al., 2009). Models were constructed to deliver care in the manner of an idealized physician. These models were tested by treating the same simulated patients that subject physicians treated. Perturbations in model processes were used to explain failure to achieve goal. These perturbations represented forms of omission bias which result in errors of under-treating patients. Each physician's dataset on each case was scored for errors and decision strategy. A mapping was developed from an error to a process perturbation. Models of each physician were constructed by: (1) selecting an idealized model that used the same decision strategy as the subject, and (2) introducing perturbations in the model based on errors that the subject committed when treating the cases. For each case each physician and corresponding model were evaluated for a goodness of fit based on similarity of error patterns committed, differences in final blood glucose values obtained, and similarity of final medications prescribed. Models made point predictions for when during the course of treatment errors would be committed: 90% of models generated the same types of errors as modeled physicians and 67% of models committed the same errors on the same visits as physicians. Based on tests of the models (theory) we found support for omission bias as a plausible explanation for agents committing errors of under-treating which prevents reaching clinical goals with type 2 diabetes patients. While the models could predict treatment actions for prescribing oral medications, it failed to accurately predict prescriptions for insulin doses.
  • Thumbnail Image
    Item
    A two-scale thermomechanical computational model for reinforced concrete frame structures
    (2014-09) DesHarnais, Marie Gisele
    A two-scale numerical model is developed to study the behavior of reinforced concrete (RC) frame structures subject to fire loading. In this model, various structural components, such as beams, columns, and beam-column joints, are modeled by elastic elements connected by a set of nonlinear cohesive elements, which represent the potential damage zones. The thermo-dependent constitutive behavior of each cohesive element is determined by nonlinear finite elements (FE) simulations of its corresponding potential damage zone under different loading modes at different temperatures, where the thermo-dependent material properties for the FE simulations are determined based on the existing literature and a set of high-temperature experiments on concrete. The proposed two-scale model is used to simulate the behavior of a RC frame subassemblage under thermomechanical loading and the simulation results are further compared with the prediction by using the conventional finite element model. It is shown that the present model can well capture the nonlinear behavior of RC frame structures under thermomechanical loading, and due to its computational efficiency, the model provides us an efficient means to investigate the global behavior of large-scale RC frame structures under fires.
  • Thumbnail Image
    Item
    Validating a computational model of patient illness: the Simcare Patient Model.
    (2012-08) McCabe, Ryan M.
    The SimCare Patient Model is a computational model of individuals with type 2 diabetes. The model represents a patient as a sequence of health states that respond to treatments over varying intervals of time. It was originally constructed as a “clinical” model of an “individual patient” with type 2 diabetes so that a physician could access the model by querying the patient state for information, ordering specific treatments for the simulated patient and scheduling the next clinical encounter. A software implementation of the model, generated by previous research (Dutta, et al. 2005), has been used as a training tool for medical residents and primary care physicians (O'Connor, Sperl-Hillen, et al., Simulated Physician Learning Intervention to Improve Safety and Quality of Diabetes Care: A Randomized Trial 2009), a guideline and protocol simulator as well as a tool for identifying optimal treatments under given constraints (McCabe, et al. 2008). This thesis contributes to the understanding of computational model validation in three ways, by: conducting a two-part validation of a model of patient illness, generating a conceptual model so that explanations can be generalized from simulations, and developing an N=1 approach to validating meaningful variation over time in individual patients with chronic disease. The validation is a two-part study of the SimCare Patient Model. The first part is a conceptual validation that defines what aspects of a real-world problem are being modeled and why. How these aspects are represented in the model as sets of variables and functions is also defined. The conceptual validation provides transparency as to the workings of the model, a basis for generalizing explanations related to model predictions or emergent behavior, and the relevant contexts for model utilization. The second part is an operational validation that conducts two sets of simulation experiments to compare model predictions to observed values. Each set of experiments is used to characterize model accuracy in different contexts: The simulation of aggregated outcomes of cohorts of patients responding to treatment protocols in controlled trials and of meaningful variation in individual patients responding to treatments in a clinical care setting. The first set of experiments compares the simulated results of three published randomized clinical trials – each with a different focus on a main aspect of treatment of patients with type 2 diabetes – using three different cohort measures: nominal intermediate health outcomes, relative intermediate health outcomes and cardiovascular disease event rates. One trial has also been simulated by multiple, alternative type 2 diabetes models and provides a basis for comparison of these models with SimCare. The second set of experiments compares actual treatments and outcomes drawn from de-identified electronic health records in a clinical care database to a range of simulated responses from identical synthetic patients and treatments over the course of a year, one patient at a time (N=1). The contributions of this thesis can be organized into three related parts, 1) a two-part validation study of a computational model of patient illness, 2) a conceptual model to be used as the basis for generating explanations for model behavior, and 3) a novel form of operational validation using an N=1 experimental approach to measure meaningful variation in individual patients over time. The validation is presented to satisfy the interests of two overlapping research communities – those interested in the content of the model: the healthcare research community; those interested in computational modeling and validation techniques: the computer science community. The validation study is divided into a conceptual validation and an operational validation. The conceptual validation establishes the set of relevant theories identified in the natural system to be represented in the model. These theories enable the explanations of the model to be generalized and learned from, and they define the intent and contexts for relevant uses of the model. The operational validation performs two types of simulation studies that characterize the outputs of the model under two different real-world contexts. The first set of experiments compares the simulation of populations of individuals under treatment protocols to the outcomes of three well-known clinical trials in the diabetes community. This distinguishes the model as being able to simulate controlled trials to the extent that a population of individuals can be generated and treatment protocols defined. In the second set of simulation experiments, a series of N=1 trials are conducted using retrospective, outpatient clinical care data to demonstrate that SimCare accurately represents meaningful variation over time in individuals being treated for diabetes in a clinical (i.e., less controlled) setting over time. In this setting, meaningful variation is defined as the non-random, clinically relevant variation in outcomes that can emerge over time given a specific course of treatment and an initial patient state. For example, if a physician were to treat two simulated patients via model software, and each patient had identical, observable initial states and received same treatments, the physician would not expect the two patients to exhibit identical responses to the treatments. This variation that exists in the real world of clinical care and causes an individual patient to exhibit meaningful differences in outcomes over time is an intentional part of the SimCare model and requires its own validation study. This distinguishes the model as being one of individual patients (rather than population-based) representing common, primary care encounters (rather than pre-screened patients under controlled protocols). The results of the conceptual validation show that the SimCare model is clinically transparent and capable of generating explanations related to treatment outcomes. The operational validation shows that the SimCare model is able to capture meaningful and typical variation both in individual patients over time and across sets of patients in controlled cohorts.