CAREER: Towards Safe and Interpretable Autonomy in Healthcare

该学院早期职业发展（CAREER）补助金将资助研究，使新知识与安全和可解释的自主药物给药在重症监护，从而促进科学的进步和推进国家健康，繁荣和福利。目前，建模、控制和测试方面的巨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，该项目旨在开发克服重症监护药物给药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整合到临床环境中奠定基础。此外，该项目致力于教育的卓越性和推广，旨在让代表性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。这些举措专注于安全和可解释的自主性，包括丰富现有的工程课程，为高中生开发教育模块，指导顶点项目，提供大学辅导，并组织定期的实验室图尔斯参观，以培养对STEM领域的兴趣并促进包容性。该研究旨在融合来自机器学习，控制系统，概率建模，和因果推理，在重症监护的自主药物给药领域进行创新。其主要目标是实现一个整体的、集成的自主框架，该框架可以解决血流动力学系统建模中的不确定性和数据稀缺性，同时确保其控制保持安全、可靠和可解释。这一办法有三个方面：首先，将开发一个整体建模框架，以估计不确定性并提高血流动力学响应的预测准确性，集成贝叶斯推理，自动编码器学习，无迹卡尔曼滤波器和贝叶斯优化。其次，将建立一种新的控制算法，重点关注给药决策的安全性和可解释性，利用贝叶斯因果模型的剂量反应见解，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场景中测试和验证这些进步，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性的安全性和有效性。该项目有望为该领域做出重大贡献，提高自主药物给药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。

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.