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I-Corps: Computational Pathophysiology-Centric Medical Guidance Systems

I-Corps: Computational Pathophysiology-Centric Medical Guidance Systems
I-Corps:以计算病理生理学为中心的医疗指导系统
批准号:
1931218
负责人:
Lui Sha
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-06-15 至 2020-11-30

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中文摘要
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英文摘要
The broader impact/commercial potential of this I-Corps project is dramatically reduced preventable medical errors, which claims 250,000 lives per year and is the third leading cause of deaths. Our medical best practice guidance system is an advanced clinical decision support (CDS) technology. Like Global Positioning System (GPS) based navigation systems that transformed transportation, the guidance systems developed here will potentially fundamentally transform the implementation of medical best practices, with rapid and consistent timing of medical interventions, preventing unintended deviation from standardized medical treatment guidelines, more accurate record keeping, and improved team situation awareness. Current market reports estimate a CDS market of more than $1 billion by 2023, with stand-alone systems accounting for the greatest market share.This I-Corps further develops a computational pathophysiology platform. Using this new computational paradigm, an informal description of the disease process in the medical text can be transformed into executable formal representations in the form of networked organ state machines and best practice state machines that can be verified against specifications and validated in clinical evaluation. The result is executable computer models and precise software that can track and records complex disease dynamics. The precise and repeatable nature of executable software model removes errors caused by subjective interpretations and memorization problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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Collaborative Research: CPS: Medium: Physics-Model-Based Neural Networks Redesign for CPS Learning and Control
CPS: Medium: Collaborative Research: Virtual Sully: Autopilot with Multilevel Adaptation for Handling Large Uncertainties
CSR: Small: Collaborative Research: Real-Time Computing Infrastructure for Integrated CPU-GPU SoC Platforms
国内基金
海外基金
Computational Methods for Analyzing Toponome Data