SBIR Phase I: An Artificial Intelligence-Inspired Computing Application for Detecting the Early Onset of Pneumonia (COVID-19)

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是开发用于检测肺炎早期发作的人工智能（AI）计算应用程序。它对COVID-19的并发症特别有用;临床研究已经确定COVID-19和肺炎之间存在显著关联，研究观察到高达70.1%的老年COVID-19患者被诊断为肺炎。这项工作旨在使用远程健康监测收集生理数据和症状决定因素，并将其传输到我们基于人工智能的云应用程序中，以检测与肺炎相关的模式。通过在医院环境外进行可访问的监测，该拟议的应用程序在最早出现恶化迹象时提供患者护理管理，并作为补充诊断工具，可用于一般检测这种危及生命的疾病-特别是对于COVID-19患者。该小型企业创新研究第一阶段项目旨在通过开发预测算法策略，为有肺炎风险的门诊COVID-19患者提供最佳护理，以应对当前COVID-19大流行的一些公共卫生挑战。拟议的应用程序使用多模态数据集（生理和用户输入）与基于云的协作AI集成。拟议的应用程序将包括一个基于云的预测分析单元，该单元从远程健康监测接收多模态信息，识别肺炎的早期发作，并向医疗保健提供者发出警报。 拟议工作的关键创新之一是动态分析单元的动态自适应方法，该方法对低维数据进行分类，并根据需要通过包括实时患者症状来扩展维度模型。这种方法为人工智能提供了一种新的协作方法，其中COVID-19患者在系统决策过程中积极协作。系统将自动决定应该从患者交互式请求什么，以提高预测准确性。该方法将提供增强的临床信息，允许临床医生监督快速反应时，该算法检测到的模式与肺炎的早期发病。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a Human-Artificial Intelligence (AI) computing application for detecting the early onset of pneumonia. It can be particularly useful for complications of COVID-19; clinical studies have identified a significant association between COVID-19 and pneumonia, with studies observing up to 70.1% of older COVID-19 patients diagnosed with pneumonia. This work aims to collect physiological data and symptomatic determinants using remote health monitoring and stream it to our AI-based cloud application to detect the pattern associated with pneumonia. Through accessible monitoring outside the hospital setting, this proposed application affords patient care management at the earliest signs of worsening and serving as a complementary diagnostic tool, useful for general detection of this life-threatening ailment - particularly for COVID-19 patients. This Small Business Innovation Research Phase I project proposes to address some of the public health challenge of the current COVID-19 pandemic by developing a predictive algorithm strategy for providing optimal care for outpatient COVID-19 patients at risk of pneumonia. The proposed application uses a multimodal dataset (physiological and user inputs) integrated with collaborative cloud-based AI. The proposed application will include a cloud-based predictive analytics unit that receives multimodal information from Remote Health Monitoring, identifies the early onset of pneumonia, and alerts healthcare providers. One of the proposed work’s key innovations is the dynamic analytics unit’s dynamically adaptive approach that performs classifications on low-dimensional data and expands the dimensionality model as needed by including real-time patient symptoms. This approach affords a novel collaborative approach to AI, where the COVID-19 patient is actively collaborating in the system decision-making process. The system will automatically decide what should be interactively requested from the patient to enhance prediction accuracy. The approach will provide enhanced clinical information, allowing for clinician oversight for rapid response when the algorithm detects a pattern associated with the early onset of pneumonia.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.