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) 患者的健康和福祉，同时节省数十亿美元的心力衰竭住院费用。如果该技术被证明是可行的，它可以将高频管理的模式从被动转变为主动。所提出的机器学习模型从临床数据中提取潜在特征并检测微妙模式，从而得出数字生物标记，从而有可能实现新的表型发现并最终实现个性化医疗。来自拟议创新的数字生物标志物在临床试验中使用时，还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能够以较小的患者群体实现所需的统计功效。反过来，这将使临床试验变得更快、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困扰超过 650 万美国人的心力衰竭 (HF) 的负担。作为美国住院治疗的主要原因，心力衰竭每年造成超过 290 亿美元的住院费用和 110 亿美元的住院费用。很大一部分住院费用是由再入院造成的，约 20% 的心力衰竭患者在出院 30 天内再入院。根本的挑战是这种疾病的变异性。对一名患者有效的治疗方案可能对另一名患者无效，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，是改善心力衰竭患者预后的关键。为了帮助临床医生预测心衰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，以预测心衰患者入院后不久的临床轨迹。该技术是基于大规模、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相当准确的预测人工智能模型，并在两个大型医疗保健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智力优点和更广泛的影响审查标准进行评估，被认为值得支持。