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

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.