Collaborative Research: FW-HTF-R: Embedding Preferences in Adaptable Artificial Intelligence Decision Support for Transplant Healthcare to Reduce Kidney Discard

合作研究：FW-HTF-R：在移植医疗保健的适应性人工智能决策支持中嵌入偏好，以减少肾脏废弃

• 批准号: 2222801
• 负责人: Casey Canfield
• 金额: $ 180万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222801&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; Embedding

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival compared to chronic dialysis. However, approximately 20% of deceased donor kidneys are discarded and never transplanted. While some discards may be medically appropriate, others reflect missed opportunities. Even kidneys deemed less desirable may provide survival benefits to some patients. Organ Procurement Organizations (OPOs) have great difficulty finding transplant centers to accept less medically desirable (higher risk) kidneys. At their discretion, OPOs can use accelerated placement to bypass the priority list for “hard-to-place” kidneys. However, due to a lack of data-driven guidance, this mechanism is not systematically applied and likely underutilized. To enable transformative change, we will integrate Artificial Intelligence (AI) decision support into the kidney offer process for both demand at the transplant center and supply at the OPO. Key workers include OPO staff (organ procurement coordinators, operations directors, medical directors), transplant center staff (coordinators, physicians, surgeons), and transplant patients. This research is driven by a partnership between transplant and ethics experts at Saint Louis University Hospital, behavioral scientists at the United Network for Organ Sharing (UNOS), and experts in AI and human factors from Missouri University of Science & Technology.Building on a FW-HTF planning grant, this project is developing an AI decision support system for (a) transplant centers to accept/deny high-risk kidney offers and (b) OPOs to identify hard-to-place kidneys sooner. This research will (1) measure worker preferences to customize the support system’s operation and interface, (2) aggregate fairness preferences as defined by diverse stakeholders to improve fairness in the model output, (3) evaluate the effect of embedding uncertainty and explainability into the interface, (4) develop deep learning ensemble models that can adapt over time while being explainable, and (5) conduct randomized control trials using UNOS Lab’s SimUNet, a realistic kidney offer simulation platform for behavioral experiments, to estimate the impact on kidney discard. Within the deep learning model, this project will impose trade-offs to increase fairness without significantly reducing accuracy, enhance explainability by converting feature relevance into linguistic expressions, and integrate new data (such as customizing for worker preferences) through transfer learning as conditions change in kidney transplant practices. Ultimately, this research aims to reduce kidney discard for “hard-to-place” organs by at least 10%. In addition, this work will support critical advancements in ethics and training, issues that will be critical in overcoming system-level barriers to integrate AI into healthcare.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.

与慢性透析相比，移植为患有末期肾脏疾病的患者具有更好的生活质量和长期生存。但是，大约20％的已故供体肾脏被丢弃，从未移植。虽然有些丢弃在医学上可能是适当的，但有些则反映了错过的机会。即使是被认为较少理想的肾脏也可能为某些患者提供生存益处。器官采购组织（OPOS）很难找到移植中心，以接受医学上理想的（较高的风险）肾脏。 OPO可酌情决定使用加速位置来绕过“难以位置”肾脏的优先级列表。但是，由于缺乏数据驱动的指导，这种机制并未系统地应用和可能未被充分利用。为了实现变革性的变化，我们将将人工智能（AI）的决策支持整合到肾脏的要约过程中，以满足移植中心的需求和OPO的供应。主要工作人员包括OPO员工（器官采购协调员，运营主管，医疗董事），移植中心工作人员（协调员，医师，外科医生）和移植患者。这项研究是由圣路易斯大学医院的移植和道德专家之间的合作伙伴关系驱动的早日难以释放肾脏。这项研究将（1）衡量工人偏好自定义支持系统的操作和界面，（2）汇总的公平性偏好是由潜水员利益相关者定义的，以提高模型输出中的公平性，（（3）评估嵌入不确定性和解释性的效果行为实验的仿真平台，以估计对肾脏丢弃的影响。在深度学习模型中，该项目将实施权衡取舍，以提高公平性而不显着降低准确性，通过将功能相关性转换为语言表达方式，并通过转移学习来综合新数据（例如定制工人偏好），因为条件变化肾脏移植实践。最终，这项研究旨在将“难以放置”器官的肾脏丢弃至少10％。此外，这项工作将支持伦理和培训方面的重要进步，这对于克服AI纳入医疗保健的系统级障碍至关重要的问题。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响标准的评估来评估的支持。

项目成果

Complex System Methodology for Meta Architecture Optimization of the Kidney Transplant System of Systems

肾移植系统元架构优化的复杂系统方法

• 发表时间: 2022
• 期刊: IEEE International Conference on System of Systems Engineering
• 作者: Threlkeld, R.;Ashiku, L.;Dagli, C.
• 通讯作者: Dagli, C.

A Use Case for Developing Meta Architectures with Artificial Intelligence and Agent Based Simulation in the Kidney Transplant Complex System of Systems

在肾移植复杂系统中使用人工智能和基于代理的模拟开发元架构的用例

• 发表时间: 2023
• 期刊: IEEE International Conference on System of Systems Engineering
• 作者: Threlkeld, R.;Ashiku, L.;Dagli, C.
• 通讯作者: Dagli, C.

Identify Hard-to-Place Kidneys for Early Engagement in Accelerated Placement With a Deep Learning Optimization Approach

通过深度学习优化方法识别难以放置的肾脏，以便尽早参与加速放置

• DOI: 10.1016/j.transproceed.2022.12.005
• 发表时间: 2023
• 期刊: Transplantation Proceedings
• 影响因子: 0.9
• 作者: Ashiku, Lirim;Dagli, Cihan
• 通讯作者: Dagli, Cihan

AI-Enabled Digital Support to Increase Placement of Hard-to-Place Deceased Donor Kidneys

支持人工智能的数字支持可增加难以放置的已故捐献肾脏的放置

• 发表时间: 2023
• 期刊: American journal of transplantation
• 影响因子: 8.8
• 作者: Threlkeld, R.;Ashiku, L.;Dagli, C.;Dzieran, R.;Canfield, C.;Lentine, K.;Schnitzler, M.;Marklin, G.;Rothweiler, R.;Speir, L.
• 通讯作者: Speir, L.