STTR Phase I: Developing a Machine Learning Platform for Workplace Wellness Programs

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力促进了一种有前途的新方法，以改善员工的健康，并有可能在工作场所之外得到更广泛的应用。这种改进的方法将创新的临床诊断与计算机辅助学习相结合，以指导建议。它提供了通过使用高效和可扩展的运营模式来遏制流行性慢性病的潜力。采用这种数据驱动的方法，预计不仅可以改善参与员工？健康，而且还降低了医疗保健费用。员工健康状况的改善也会带来生产力的提高。因此，更好的健康和经济储蓄，以美国公司和工作的美国人是这个项目的现实目标。因此，雇主和公众有望从这一举措中受益。拟议项目结合了新颖而经济的临床诊断方法，并通过计算机学习得到加强，以有效改善工作场所的健康。传统的健康策略目前在解决日益增长的慢性病流行方面既低效又无效。这些慢性疾病包括但不限于：糖尿病、肥胖症、高血压和心血管疾病。该项目的主要目标是整合各种临床发现，通过机器学习加强慢性疾病的预防。这种独特的方法有助于开发有效且负担得起的可扩展的工作场所健康计划。小说平台不断向所有参与者学习？它能够自动分析干预结果，并智能地为每个参与者发现最佳的预防干预计划。该平台创新性地采用低成本定量诊断成像来跟踪与健康相关的临床关键健康参数。计算机辅助建模包括动态生理模型（即当前症状，生活方式测量，过去的病史，MRI扫描和实验室检查），以临床评估慢性病的病程沿着各种预防干预策略。通过机器学习增强的临床结果的测量确定了重要的生物标志物，以便为参与个体制定干预建议。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project promotes a promising new approach to improve employee wellness with the potential for wider application beyond the workplace. This improved approach combines both innovative clinical diagnostics with computer assisted learning to guide recommendations. It offers the potential to curb epidemic chronic disease through the use of efficient as well as scalable operating models. Adopting this data-driven approach is expected to improve not only participating employees? health but also reduce health care costs. Increased productivity which follows from improved employee health can also be expected. Accordingly, better health and economic savings to both corporate America and working Americans are realistic objectives of this project. Employers and the general public therefore are expected to benefit from this initiative.The proposed project combines novel as well as economic use of clinical diagnostics, strengthened by computer learning, to effectively improve workplace wellness. Conventional wellness strategies are presently both inefficient as well as ineffective in addressing the growing epidemic of chronic diseases. These chronic diseases include but are not limited to: diabetes, obesity, high blood pressure and cardiovascular diseases. The principal objective of this project is the integration of various clinical findings in prevention of chronic disease enhanced by machine learning. This unique approach aids in the development of scalable, workplace wellness programs which are effective and affordable. The novel platform continuously learns from all participants? interventional results and intelligently discovers the best preventive intervention plan for each participant. The platform innovatively adopts low cost quantitative diagnostic imagery to track key health parameters clinically correlated to wellness. Computer aided modeling includes dynamic physiological models (i.e. current symptoms, lifestyle measurements, past medical history, MRI scans and laboratory tests) to clinically evaluate the course of chronic disease along with various prevention strategies for intervention. Measures of clinical outcome enhanced by machine learning identify important biomarkers to develop intervention recommendations for participating individuals.