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.

这项小型企业创新研究（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）患者的健康和福祉，同时节省数十亿美元的HF住院费用。如果该技术被证明是可行的，它可以将HF管理的范式从被动转向主动。所提出的机器学习模型提取潜在特征并从临床数据中检测细微模式，从而获得数字生物标志物，这些生物标志物可能有助于发现新的表型并最终实现个性化医疗。从拟议的创新中获得的数字生物标志物在用于临床试验时，也可以提高新疗法在应用于不同人群时的包容性和更大的普遍性。所提出的技术可以使临床试验申办者在较小的患者群体中实现所需的统计功效。这反过来将使临床试验更快、更便宜、更有效。这个小企业创新研究（SBIR）第一阶段项目减轻了心力衰竭（HF）的负担，HF困扰着650多万美国人。作为美国住院的主要原因，HF每年导致超过290亿美元的医院费用和110亿美元的住院费用。住院费用的很大一部分是由再入院引起的，大约20%的心力衰竭患者在出院后30天内再次入院。最根本的挑战是这种疾病的变异性。对一个病人有效的治疗方案可能对另一个病人无效，即使他们表现出相似的症状。预测临床轨迹、治疗反应和潜在并发症，并将这些见解转化为可行的干预措施，是改善HF患者结局的关键。为了帮助临床医生预测HF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概括的多模式人工智能（AI）模型，用于预测HF患者入院后不久的临床轨迹。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一个合理准确的预测AI模型，在两个大型医疗系统的数据之间进行交叉验证。该奖项反映了NSF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。