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Research Topic : CLINICAL OUTCOMES
Status : Active
Scheme : Discovery Projects
Australian State/Territory : ACT
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  • Researchers (28)
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP200100726

    Funder
    Australian Research Council
    Funding Amount
    $444,353.00
    Summary
    Assessing the national productivity impacts of chronic ill health. The project aims to address one of the biggest gaps in health and productivity research by designing a novel composite national metric that will rank lost productivity due to chronic illness The project brings together tax/transfer modelling, health modelling and epidemiological modelling specialists to develop a highly innovative microsimulation model: Health&WorkMOD to then quantify the costs of health-related productivity loss .... Assessing the national productivity impacts of chronic ill health. The project aims to address one of the biggest gaps in health and productivity research by designing a novel composite national metric that will rank lost productivity due to chronic illness The project brings together tax/transfer modelling, health modelling and epidemiological modelling specialists to develop a highly innovative microsimulation model: Health&WorkMOD to then quantify the costs of health-related productivity loss. The proposed model, an international first, will be a powerful tool to comprehensively model the cost impacts of illness and simulate policy options related to health and productivity. This will provide answers to critical policy questions for government with potential significant economic benefits.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP160100494

    Funder
    Australian Research Council
    Funding Amount
    $515,000.00
    Summary
    Architectural design to improve Indigenous health outcomes. The project seeks to develop evidence-based knowledge on what Indigenous clients find supportive or stressful in health care settings, to formulate recommendations for architectural design and service delivery. Many Indigenous people fail to present for health care until chronically ill, due to fear or dislike of health services and their settings. This project aims to understand how design in healthcare architecture across different bu .... Architectural design to improve Indigenous health outcomes. The project seeks to develop evidence-based knowledge on what Indigenous clients find supportive or stressful in health care settings, to formulate recommendations for architectural design and service delivery. Many Indigenous people fail to present for health care until chronically ill, due to fear or dislike of health services and their settings. This project aims to understand how design in healthcare architecture across different building scales and services (clinics, hospitals, waiting rooms, wards etc) affects Indigenous people’s use and perceptions of these environments and consequent motivation to access health care services. Developing innovative and adaptable research methods, the project seeks to identify the necessary architectural design changes for health settings to facilitate access for Indigenous people.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200103223

    Funder
    Australian Research Council
    Funding Amount
    $396,000.00
    Summary
    Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored .... Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.
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    Showing 1-3 of 3 Funded Activites

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