SBIR Phase I: Driving Timely Point-of-Care Treatment in Hospitals with a High Precision Bayesian Machine Learning Platform

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是提供临床决策支持软件，帮助住院医生改善对可预防的急性住院伤害的护理，从而降低死亡率和发病率。该基金开发了一个基于云的平台，该平台可以实时应用机器学习（ML）算法，处理从电子健康记录（EHRs）和附着在患者身上的生理监测设备中提取的数据。所使用的机器学习工具在收集和整合这些信号时估计每个数据元素的可靠性程度，以提供准确的、个性化的患者健康风险估计，以便最佳地指导患者治疗和医院资源分配。我们最初的目标是败血症，这是医院里最昂贵、最致命的疾病之一。这笔赠款开发了一个端到端系统，以提供风险评估和及时实施治疗。对于商业潜力，底层核心技术可以扩展到其他临床场景。拟议的项目能够扩展高精度的最先进的贝叶斯机器学习技术，预测急性恶化的可能性。这包括解决将机器学习系统扩展到许多护理提供者、患者和医院的挑战。为了实现这些目标，该项目将开发在云计算环境中以分布式方式运行机器学习算法的新方法，特别是在区分多台机器需要协调的地方，更重要的是，它们可以避免在数据训练中协调。此外，该项目还开发了软件，将信息反馈给医疗服务提供者，从而使干预措施能够改变患者的轨迹。在这里，该软件将包括如何最好地利用由此产生的推论来指导护理。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide clinical decision support software that assists inpatient providers in improving care for preventable acute inpatient harms, and thereby reduce mortality and morbidity. This grant develops a cloud-based platform that applies machine learning (ML) algorithms in real-time on data extracted from Electronic Health Records (EHRs) and physiologic monitoring devices attached to a patient. The ML tools employed estimate the degree of reliability for each of the data elements as they are collected and integrates these signals to provide an accurate, individualized risk estimate of patient health over time in order to best guide patient treatment and allocation of hospital resources. Our initial target condition is sepsis, one of the most costly and most deadly diseases in hospitals. This grant develops an end-to-end system to provide risk assessment and implementation of timely treatment. For commercial potential, the underlying core technology can be extended to other clinical scenarios. The proposed project enables scaling of high-precision state-of-the-art Bayesian machine learning techniques that forecast the chance of acute deterioration. This includes tackling the challenges in scaling this machine learning system to function across many care providers, patients, and hospitals. To achieve these goals, this project will develop new methods for running machine learning algorithms in a distributed fashion in cloud computing settings, especially in distinguishing where multiple machines need to coordinate, and arguably more importantly, where they can avoid coordinating in training on data. Further, the project develops software to provide information back to providers so as to enable interventions that can alter patient trajectory. Here the software will encompass how to best use the resulting inferences in guiding care.