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

这一小型企业技术转让(STTR)第一阶段项目的更广泛影响/商业潜力促进了一种有前景的新方法，以改善员工健康，并有可能在工作场所以外得到更广泛的应用。这种改进的方法结合了创新的临床诊断和计算机辅助学习来指导建议。它提供了通过使用高效和可扩展的业务模式来遏制流行的慢性疾病的潜力。采用这种以数据为导向的方法，不仅有望改善参与其中的员工？而且还能降低医疗保健成本。随着员工健康状况的改善，生产率的提高也是可以预期的。因此，为美国企业和美国工薪阶层提供更好的健康和经济储蓄是该项目的现实目标。因此，雇主和一般市民可望从这项措施中受惠。拟议的计划结合了临床诊断学的新颖和经济用途，并透过电脑学习，有效地改善工作场所的健康。在应对日益严重的慢性病流行方面，传统的保健战略目前既效率低下，也无效。这些慢性病包括但不限于：糖尿病、肥胖症、高血压和心血管疾病。这个项目的主要目标是整合各种临床结果，通过机器学习加强对慢性病的预防。这一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。新奇平台不断向所有参与者学习？干预结果，并智能地为每个参与者找到最佳的预防性干预计划。该平台创新性地采用低成本定量诊断图像来跟踪临床上与健康相关的关键健康参数。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、既往病史、核磁共振扫描和实验室测试)，用于临床评估慢性病的过程以及各种干预预防策略。通过机器学习增强的临床结果的测量确定重要的生物标记物，以制定对参与的个人的干预建议。

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.