Browsing by Subject "Computational modeling"

Now showing 1 - 4 of 4
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    Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance
    (Elsevier, 2015-10) Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.; BioHealth Informatics, School of Informatics and Computing
    RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.
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    Individual associations of adolescent alcohol use disorder versus cannabis use disorder symptoms in neural prediction error signaling and the response to novelty
    (Elsevier, 2021-04) Aloi, Joseph; Crum, Kathleen I.; Blair, Karina S.; Zhang, Ru; Bashford-Largo, Johannah; Bajaj, Sahil; Schwartz, Amanda; Carollo, Erin; Hwang, Soonjo; Leiker, Emily; Filbey, Francesca M.; Averbeck, Bruno B.; Dobbertin, Matthew; Blair, R. James R.; Psychiatry, School of Medicine
    Two of the most commonly used illegal substances by adolescents are alcohol and cannabis. Alcohol use disorder (AUD) and cannabis use disorder (CUD) are associated with poorer decision-making in adolescents. In adolescents, level of AUD symptomatology has been negatively associated with striatal reward responsivity. However, little work has explored the relationship with striatal reward prediction error (RPE) representation and the extent to which any augmentation of RPE by novel stimuli is impacted. One-hundred fifty-one adolescents participated in the Novelty Task while undergoing functional magnetic resonance imaging (fMRI). In this task, participants learn to choose novel or non-novel stimuli to gain monetary reward. Level of AUD symptomatology was negatively associated with both optimal decision-making and BOLD response modulation by RPE within striatum and regions of prefrontal cortex. The neural alterations in RPE representation were particularly pronounced when participants were exploring novel stimuli. Level of CUD symptomatology moderated the relationship between novelty propensity and RPE representation within inferior parietal lobule and dorsomedial prefrontal cortex. These data expand on an emerging literature investigating individual associations of AUD symptomatology levels versus CUD symptomatology levels and RPE representation during reinforcement processing and provide insight on the role of neuro-computational processes underlying reinforcement learning/decision-making in adolescents.
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    Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm
    (IEEE Xplore, 2021-08) Qu, Xidi; Wang, Shengling; Hu, Qin; Cheng, Xiuzhen; Computer and Information Science, School of Science
    Proof of work (PoW), the most popular consensus mechanism for blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-mining, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our article is the first work to employ federal learning as the proof of work for blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.
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    Sizing it up: The mechanical feedback hypothesis of organ growth regulation
    (Elsevier, 2014) Buchmann, Amy; Alber, Mark; Zartman, Jeremiah J.; Medicine, School of Medicine
    The question of how the physical dimensions of animal organs are specified has long fascinated both experimentalists and computational scientists working in the field of developmental biology. Research over the last few decades has identified many of the genes and signaling pathways involved in organizing the emergent multi-scale features of growth and homeostasis. However, an integrated model of organ growth regulation is still unrealized due to the numerous feedback control loops found within and between intercellular signaling pathways as well as a lack of understanding of the exact role of mechanotransduction. Here, we review several computational and experimental studies that have investigated the mechanical feedback hypothesis of organ growth control, which postulates that mechanical forces are important for regulating the termination of growth and hence the final physical dimensions of organs. In particular, we highlight selected computational studies that have focused on the regulation of growth of the Drosophila wing imaginal disc. In many ways, these computational and theoretical approaches continue to guide experimental inquiry. We demonstrate using several examples how future progress in dissecting the crosstalk between the genetic and biophysical mechanisms controlling organ growth might depend on the close coupling between computational and experimental approaches, as well as comparison of growth control mechanisms in other systems.