FW-HTF-P: Teaming Transplant Professionals and Artificial Intelligence Tools to Reduce Kidney Discard

FW-HTF-P：联合移植专业人员和人工智能工具减少肾脏废弃

• 批准号: 2026324
• 负责人: Casey Canfield
• 金额: $ 15万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026324&HistoricalAwards=false
• 关键词: FW; HTF; Teaming; Transplant; Professionals

Thousands of procured kidneys are discarded each year due to inefficient workflow processes and negative perceptions for using lower quality or higher risk organs. While some of this discard is medically necessary, some represents lost opportunities to get patients off of dialysis and increase lifespans. This Future of Work at the Human Technology Frontier planning grant project aims to transform the organ transplant matching process. The future technology is an artificial intelligence (AI) system with usable trustworthy interfaces that are fully integrated into the transplant healthcare work context between the demand-side (transplant center) and supply-side (organ procurement organization). The future workers are organ procurement coordinators and transplant coordinators, physicians and surgeons. A single kidney can have thousands of offers before one, if any, transplant center accepts it. The current process of manually placing lower quality organs exacerbates lost opportunities. The AI system will identify transplant teams and candidates that are most likely to accept a lower quality organ so that the match can be identified quickly and the organ is less likely to be discarded. This planning grant will build capacity for integrating AI into transplant healthcare and engage workers as well as pre- and post-transplant patients in a design-a-thon event. This research is driven by transplant experts at Saint Louis University Hospital and experts in AI and human factors from Missouri University of Science & Technology. Once validated, this research can also be applied to other data-intensive high-stakes scenarios (e.g. military operations, critical infrastructure).AI systems often suffer from technical, human, and integration challenges. Over the course of the planning grant, we will (1) document a transplant work system architecture and identify challenges for re-designing this work process, (2) develop a proof-of-concept AI system to predict which candidates are most likely to accept a lower quality kidney that is at risk of discard, and (3) perform human subjects experiments to scope the interface design and predict technology adoption factors. It is time-consuming and costly to manually design neural architectures, so this project proposes an approach that uses evolutionary algorithms to find the optimal architecture for a particular data set. This will facilitate real-time adaptation as the data inputs evolve over time. In addition, it is critical for AI systems to be explainable and transparent, particularly in high stakes contexts. The project will perform human subject experiments with lay populations to evaluate how uncertainty visualizations and metrics influence performance, confidence, trust, technology acceptance, and willingness to choose riskier options.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.

由于工作流程效率低下以及对使用低质量或高风险器官的负面看法，每年有数千个采购的肾脏被丢弃。虽然其中一些丢弃是医学上必要的，但有些代表着失去了让患者摆脱透析和延长寿命的机会。人类技术前沿规划拨款项目的未来工作旨在改变器官移植匹配过程。未来的技术是一个人工智能（AI）系统，具有可用的可信接口，完全集成到需求方（移植中心）和供应方（器官采购组织）之间的移植医疗工作环境中。未来的工作者是器官采购协调员和移植协调员，内科医生和外科医生。一个肾脏可能会有成千上万的人提供，然后才有一个移植中心接受它。目前手动放置质量较低的器官的过程加剧了机会的丧失。人工智能系统将识别最有可能接受较低质量器官的移植团队和候选人，以便快速识别匹配，并且器官不太可能被丢弃。这项规划拨款将建立将人工智能整合到移植医疗保健中的能力，并让工作人员以及移植前后的患者参与设计通村活动。这项研究由圣刘易斯大学医院的移植专家和密苏里州科技大学的人工智能和人为因素专家推动。一旦得到验证，这项研究也可以应用于其他数据密集型的高风险场景（例如军事行动，关键基础设施）。人工智能系统通常面临技术，人力和集成挑战。在规划拨款的过程中，我们将（1）记录移植工作系统架构，并确定重新设计该工作流程的挑战，（2）开发概念验证人工智能系统，以预测哪些候选人最有可能接受有丢弃风险的低质量肾脏，以及（3）进行人体受试者实验，以确定界面设计的范围并预测技术采用因素。人工设计神经架构既耗时又昂贵，因此该项目提出了一种使用进化算法为特定数据集找到最佳架构的方法。这将有助于随着数据输入随时间的推移而进行实时调整。此外，人工智能系统的可解释性和透明性至关重要，特别是在高风险环境中。该项目将与非专业人群进行人类受试者实验，以评估不确定性可视化和指标如何影响性能，信心，信任，技术接受度以及选择风险更高选项的意愿。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Communicating Uncertain Information from Deep Learning Models in Human Machine Teams

在人机团队中交流来自深度学习模型的不确定信息

• 发表时间: 2020
• 期刊: Proceedings of the American Society for Engineering Management 2020 International Annual Conference
• 作者: Subramanian, H. V.;Canfield, C;Shank, D. B.;Andrews, L.;Dagli, C.
• 通讯作者: Dagli, C.

Machine Learning Models and Big Data Tools for Evaluating Kidney Acceptance

• DOI: 10.1016/j.procs.2021.05.019
• 发表时间: 2021
• 期刊: Procedia Computer Science
• 作者: Lirim Ashiku;Md Al-Amin;S. Madria;C. Dagli

Reducing Kidney Discard With Artificial Intelligence Decision Support: the Need for a Transdisciplinary Systems Approach.

• DOI: 10.1007/s40472-021-00351-0
• 发表时间: 2021
• 期刊: Current transplantation reports
• 影响因子: 2.1
• 作者: Threlkeld R;Ashiku L;Canfield C;Shank DB;Schnitzler MA;Lentine KL;Axelrod DA;Battineni ACR;Randall H;Dagli C
• 通讯作者: Dagli C