SBIR Phase II: Determination of complex outcome measures using narrative clinical data to enable observational trials

这项小企业创新研究（SBIR）二期项目的更广泛影响/商业潜力是开发一种软件应用程序，用于识别临床结果，以支持高有效性的真实世界证据（RWE）。确保数据准确性和协议有效性的方法对于维护医疗保健中的安全性和有效性至关重要。该项目将分析电子健康数据，为患有多种可能混淆治疗的疾病的患者提供个性化治疗计划的有临床意义的信息。这项技术将满足尚未满足的需求，以改善患者的治疗效果，改善医疗保健服务和慢性病护理管理，并降低患者的医疗保健成本。这项技术还可以改善治疗方法的监管和报销决策。该SBIR II期项目将解决考虑使用额外健康数据的需求，以便为患有多种合并症的患者制定个性化的治疗计划。针对乳腺癌或高血压等广泛的疾病，目前还没有基于随机对照试验结构的精确医学的亚组分析或个性化治疗计划。本提案旨在确定非结构化电子健康记录（EHR）的临床结果。建议的工作是开发使用自然语言处理和推理的分析方法，以利用来自真实世界证据（RWE）和观察性研究的大量健康数据来补充随机对照试验（RCT）提供的数据。分析工具将允许在确定的患者队列中比较各种治疗方案的有效性，并为患有多种合并症的个体患者制定个性化的治疗计划。任务包括：1)利用自然语言处理（NLP）提取的语言短语来识别与结果相关的临床发现，并作为临床特征元数据维护；2)将NLP与推理相结合，准确识别候选临床结局；3)应用机器学习和专家知识来准确定义复杂的结果测量。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to develop a software application that identifies clinical outcomes to support high validity real-world evidence (RWE). Approaches to ensure data accuracy and protocol validity are critical to maintain safety and efficacy in healthcare. This project will analyze electronic health data to generate clinically meaningful information for personalized treatment plans for patients with multiple conditions that can confound treatment. This technology will fulfill an unmet need to improve patient outcomes, improve healthcare delivery and chronic disease care management, and reduce healthcare costs for patients. This technology can also improve regulatory and reimbursement decision-making for therapeutic approaches.This SBIR Phase II project will address the need for consideration of using additional health data to allow for individualized personalized therapeutic plans for patients with multiple co-morbidities. Subgroup analysis or individualized therapy plans for precision medicine are currently not available based upon the structure of randomized controlled trials for broad conditions like breast cancer or hypertension. This proposal seeks to identify clinical outcomes from unstructured Electronic Health Records (EHR). The proposed work is to develop analytics using natural language processing and inference to leverage the large amounts of health data from real-world evidence (RWE) and observational studies to augment data provided in randomized controlled trials (RCT). The analytic tools will allow a comparison of the effectiveness of various treatment protocols in defined cohorts of patients and develop a personalized treatment plan for an individual patient with multiple co-morbidities. The tasks include: 1) Leverage linguistic phrases extracted by natural language processing (NLP) to recognize outcome-related clinical findings to be maintained as clinical feature metadata; 2) Combine NLP and inference to accurately identify candidate clinical outcomes; and 3) Apply machine-learned and expert knowledge to accurately define complex outcome measures.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.