CAREER: Towards Safe and Interpretable Autonomy in Healthcare

这项教师早期职业发展(Career)补助金将资助研究，使危重护理中与安全和可解释的自主用药有关的新知识得以实现，从而促进科学进步，促进国家健康、繁荣和福利。目前，在建模、控制和测试方面的重大挑战阻碍了理论自主性在实际医疗保健中的应用。通过专注于输液治疗，该项目旨在发展克服重症监护药物剂量自主性障碍的基础能力。该项目的成功完成将为将自主算法无缝地纳入临床环境奠定基础。此外，该项目致力于教育的卓越和推广，旨在使代表人数不足的少数群体参与进来，并为各级学生提供全面的学习和培训机会。这些举措以安全和可解释的自主性为重点，包括丰富现有的工程课程，为高中生开发教育模块，指导顶峰项目，提供大学指导，以及组织定期的实验室参观，以培养人们对STEM领域的兴趣并促进包容性。研究旨在融合机器学习、控制系统、概率建模和因果推理的见解，在重症监护自主用药领域进行创新。主要目标是实现一个全面的、集成的自主框架，该框架考虑到血流动力学系统建模中的不确定性和数据稀缺，同时确保它们的控制保持安全、可靠和可解释。该方法有三个方面：首先，将开发一个整体建模框架，以估计血液动力学响应的不确定性并提高预测精度，集成贝叶斯推理、自动编码器学习、无味卡尔曼滤波和贝叶斯优化。其次，将建立一种新的控制算法，重点关注剂量决策的安全性和可解释性，利用贝叶斯因果模型中的剂量反应洞察力，并在以安全为中心的框架内优化强化学习策略。最后，该项目将在各种重症监护方案中测试和验证这些进展，特别是在循环休克的管理方面，并开发新的测试方法来评估自主的安全性和有效性。该项目将为该领域做出重大贡献，提高自主药物剂量系统的可靠性和可解释性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) grant will fund research that enables new knowledge related to safe and interpretable autonomous medication dosing in critical care, thereby promoting the progress of science and advancing national health, prosperity, and welfare. Substantial challenges in modeling, control, and testing currently impede the application of theoretical autonomy in practical healthcare. By concentrating on infusion therapy, the project intends to develop foundational capabilities for overcoming autonomy barriers in critical care drug dosing. The successful completion of this project will set the stage for the seamless incorporation of autonomous algorithms into clinical settings. Furthermore, the project is committed to educational excellence and outreach, aiming to engage underrepresented minorities and provide comprehensive learning and training opportunities for students at all levels. Focusing on the safe and interpretable autonomy, these initiatives include enriching the existing engineering curriculum, developing educational modules for high school students, mentoring capstone projects, providing college coaching, and organizing regular lab tours to foster interest in STEM fields and promote inclusivity.The research aims to merge insights from machine learning, control systems, probability modeling, and causal inference to innovate in the domain of autonomous medication dosing for critical care. The primary objective is to enable a holistic, integrated autonomous framework that accounts for uncertainty and data scarcity in the modeling of hemodynamic systems, while ensuring their control remains safe, reliable, and interpretable. The approach is threefold: First, a holistic modeling framework will be developed to estimate uncertainties and enhance prediction accuracy for hemodynamic responses, integrating Bayesian inference, autoencoder learning, the unscented Kalman filter, and Bayesian optimization. Second, a novel control algorithm will be established, focusing on the safety and interpretability of dosing decisions, leveraging dose-response insights from Bayesian causal models, and optimizing reinforcement learning policies within a safety-centric framework. Lastly, the project will test and validate these advancements across various critical care scenarios, particularly in the management of circulatory shocks, and develop new testing methodologies to assess the safety and effectiveness of the autonomy. This project is poised to contribute significantly to the field, enhancing the reliability and interpretability of autonomous medication dosing 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.