SBIR Phase I: Developing Artificial intelligence Models to Predict In-hospital Clinical Trajectories for Heart Failure Patients

SBIR 第一阶段：开发人工智能模型来预测心力衰竭患者的院内临床轨迹

• 批准号: 2304358
• 负责人: Ruizhi Liao
• 金额: $ 27.5万
• 依托单位: EMPALLO, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304358&HistoricalAwards=false
• 关键词: SBIR; Phase; Developing; Artificial; intelligence

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project includes improving cardiovascular management, personalized medicine, inclusivity for historically underserved populations, and clinical trial design. The project could improve the health and wellbeing of heart failure (HF) patients while saving billions of dollars in HF hospitalization costs. If the technology proves feasible, it could shift the paradigm of HF management from reactive to proactive. The proposed machine learning model extracts latent features and detects subtle patterns from clinical data, which derives digital biomarkers that can potentially enable novel phenotype discovery and eventually personalized medicine. The digital biomarkers derived from the proposed innovation, when used in clinical trials, could also improve inclusivity and greater generalizability of novel therapies when applied to diverse populations. The proposed technology could enable clinical trial sponsors to achieve the desired statistical power with smaller patient populations. This, in turn, would enable faster, cheaper, and more effective clinical trials.This Small Business Innovation Research (SBIR) Phase I project mitigates the burden of heart failure (HF), which afflicts over 6.5 million Americans. As the leading cause of hospitalization in the U.S., HF results in more than $29 billion in hospital charges and $11 billion in hospitalization costs, annually. A large portion of hospitalization costs are driven by readmissions, with about 20% of heart failure patients readmitted within 30 days of discharge. The fundamental challenge is the variability of this disease. A treatment regimen that works for one patient might not work for another, even if they show similar symptoms. Anticipating clinical trajectories, treatment response, and potential complications, and translating those insights into actionable interventions is key to improving outcomes for HF patients. To help clinicians anticipate a HF patient’s response to treatment and adverse events during hospitalization and enable personalized intervention planning, this project will develop explainable and generalizable multimodal artificial intelligence (AI) models that predict a HF patient’s clinical trajectory shortly after admission. This technology is a methodological innovation grounded in large-scale, multi-center, clinical data. The key milestone in Phase I is to yield a reasonably accurate predictive AI model, cross-validated between the data of two large healthcare systems.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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响包括改善心血管管理、个性化医疗、对历史上服务不足的人群的包容性以及临床试验设计。该项目可以改善心力衰竭 (HF) 的健康和福祉。如果该技术被证明可行，它可以将心力衰竭管理的模式从被动转变为主动。所提出的机器学习模型可以提取潜在特征并从临床数据中检测微妙的模式，从而得出数字生物标记。那所提出的创新所产生的数字生物标志物在应用于临床试验时，有可能实现新的表型发现并最终实现个性化医疗，当应用于不同人群时，还可以提高新疗法的包容性和更大的普遍性。以较小的患者群体实现所需的统计功效，这反过来又可以实现更快、更便宜、更有效的临床试验。这个小型企业创新研究 (SBIR) 一期项目减轻了心力衰竭 (HF) 的负担。折磨超过心力衰竭是美国 650 万美国人住院的主要原因，每年造成超过 290 亿美元的住院费用和 110 亿美元的住院费用，其中很大一部分是由再入院造成的，其中约 20% 是由心脏病引起的。失败的患者在出院后 30 天内重新入院，其根本挑战是这种疾病的可变性，即使对一名患者有效的治疗方案也可能不适用于另一名患者。反应和潜在并发症，并将这些见解转化为可行的干预措施是改善心力衰竭患者预后的关键。为了帮助预测心力衰竭患者在住院期间对治疗和不良事件的反应并实现个性化干预计划，该项目将开发可解释且可推广的多模式。人工智能 (AI) 模型可在入院后不久预测心力衰竭患者的临床轨迹。这项技术是一项基于大规模、多中心临床数据的方法创新，第一阶段的关键里程碑是产生相当准确的预测人工智能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。