Learning Components of Computational Models from Texts

The mental models of experts can be encoded in computational cognitive models that can support the functioning of intelligent agents. This paper compares human mental models to computational cognitive models, and explores the extent to which the latter can be acquired automatically from published sources via automatic learning by reading. It suggests that although model components can be automatically learned, published sources lack sufficient information for the compilation of fully specified models that can support sophisticated agent capabilities, such as physiological simulation and reasoning. Such models require hypotheses and educated guessing about unattested phenomena, which can be provided only by humans and are best recorded using knowledge engineering strategies. This work merges past work on cognitive modeling, agent simulation, learning by reading, and narrative structure, and draws examples from the domain of clinical medicine

Knowledge-Based Modeling and Simulation of Diseases with Highly Differentiated Clinical Manifestations

Abstract. This paper presents the cognitive model of gastroesophageal reflux disease (GERD) developed for the Maryland Virtual Patient simulation and mentoring environment. GERD represents a class of diseases that have a large number of clinical manifestations. Our model at once manages that complexity while offering robust automatic function in response to open-ended user actions. This ontologically grounded model is largely based on script-oriented representations of causal chains reflecting the actual physiological processes in virtual patients. A detailed description of the GERD model is presented along with a high-level description of the environment for which it was developed

Elevated suppressor of cytokine signaling-1 (SOCS-1): a mechanism for dysregulated osteoclastogenesis in HIV transgenic rats

Accelerated bone loss leading to osteopenia, osteoporosis, and bone fracture is a major health problem that is increasingly common in human immunodeficiency virus (HIV) infected patients. The underlying pathogenesis is unclear but occurs in both treatment naïve and individuals receiving antiretroviral therapies. We developed an HIV-1 transgenic rat that exhibits many key features of HIV disease including HIV-1 induced changes in bone mineral density (BMD). A key determinant in the rate of bone loss is the differentiation of osteoclasts, the cells responsible for bone resorption. We found HIV-1 transgenic osteoclast precursors (OCP) express higher levels of suppressor of cytokine signaling-1 (SOCS-1) and TNF receptor associated factor 6 (TRAF6) and are resistant to interferon-gamma (IFN-γ) mediated suppression of osteoclast differentiation. Our data suggest that dysregulated SOCS-1 expression by HIV-1 transgenic OCP promotes osteoclastogenesis leading to the accelerated bone loss observed in this animal model. We propose that elevated SOCS-1 expression in OCP antagonizes the inhibitory effects of IFN-γ and enhances receptor activator of NF-kB ligand (RANKL) signaling which drives osteoclast differentiation and activation. Understanding the molecular mechanisms of HIV-associated BMD changes has the potential to detect and treat bone metabolism disturbances early and improve the quality of life in patients

Cognitive Simulation in Virtual Patients

We present an overview of the Virtual Patient project at the University of Maryland, which is developing a cognitive model of humans experiencing various states of health and disease to be used in interactive simulations for physician training. Overview This Virtual Patient 1 project is devoted to creating a cognitive, knowledge-based model of a virtual patient (VP) that undergoes both normal and pathological physiological processes. VPs are ontological objects, specifically, subclasses of VIRTUAL-HUMAN that have various diseases and disorders. Like all VIRTUAL-HUMANs, their large inventory of property-value pairs changes in response to ontological events, includin

Flipped classroom for academic and career advising: an innovative technique for medical student advising

Introduction: Career advising for medical students can be challenging for both the student and the adviser. Our objective was to design, implement, and evaluate a "flipped classroom" style advising session. Methods: We performed a single-center cross-sectional study at an academic medical center, where a novel flipped classroom style student advising model was implemented and evaluated. In this model, students were provided a document to review and fill out prior to their one-on-one advising session. Results: Ninety-four percent (95% CI, 88%-100%) of the medical students surveyed felt that the advising session was more effective as a result of the outline provided and completed before the session and that the pre-advising document helped them gain a better understanding of the content to be discussed at the session. Conclusion: Utilization of the flipped classroom style advising document was an engaging advising technique that was well received by students at our institution.Open access journal.UA Open Access Publishing Fund.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]