SCH: INT: Improving Care for Heart Failure Patients Using Tropical Geometry and Soft Computing

该项目将开发新的计算方法，以利用领域专业知识和电子健康数据来生产具有透明建议的临床决策支持系统。这些技术将用于确定心力衰竭（HF）患者先进治疗的最佳时机，例如心脏移植和耐用的机械循环支持（MCS）设备。虽然这种疗法有能力提高患者的生存率和生活质量，但临床医生识别适当候选人并提供最佳时机治疗的能力仍然有限。该项目将通过创建一个新的机器学习范式来解决这个问题，该范式使用基于热带几何的数学公式，将近似领域知识直接纳入模型训练，然后可以使用有限的数据集进行优化。从训练模型中提取的优化规则可由临床医生解释，并可用于指导治疗决策。这些工具有望改善患者的生活，同时降低未来的成本。该项目还将为计算机辅助决策支持系统建立一个跨学科的学习平台，为学生、博士后和早期职业临床科学家将数据科学技术应用于医疗决策支持做好准备。该项目还将通过招收新学生并将研究培训融入高度多样化的实验室，将科学、技术、工程和数学（STEM）领域代表性不足的群体纳入其中。该项目将把新兴的热带几何领域应用于软计算方法。这种方法将通过以下方式避免诸如模糊逻辑的常规软计算范例的缺点：a）减少对大量训练示例的需要，B）允许在模型训练期间平滑且快速的优化，以及c）使得能够系统地减小参数空间的大小，从而降低过拟合数据的可能性。将从心脏病专家小组收集近似规则，以确定高级治疗的候选资格。使用计划的方法，这些规则将被纳入一个模型，以预测HF的进展，并确定哪些患者有资格并最有可能从心脏移植或耐用MCS器械中受益。将从训练模型中提取优化规则，并由心脏病专家小组验证其正确性和临床实用性。此外，拟议的机器学习范例将能够生成新颖和可解释的临床规则，增加我们对如何最好地管理晚期HF患者的理解。该奖项反映了NSF的法定使命，并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，被认为值得支持。

The project will develop novel computational approaches for leveraging domain expertise and electronic health data for the production of clinical decision support systems with transparent recommendations. These techniques will be used to identify the optimal timing of advanced therapies for patients with heart failure (HF), such as heart transplantation and durable mechanical circulatory support (MCS) devices. While such therapies have the ability to improve patient survival and quality of life, clinicians’ abilities to identify appropriate candidates and deliver optimally timed therapies remains limited. This project will address this problem by creating a novel machine learning paradigm, using mathematical formulations based on tropical geometry, that incorporates approximate domain knowledge directly into model training, which can then be optimized using a limited data set. Optimized rules extracted from the trained model are interpretable by clinicians and can be used to guide treatment decisions. Such tools offer the promise of improving patients’ lives while reducing future costs. The project will also establish an interdisciplinary learning platform for computer-assisted decision support systems that will prepare students, postdocs, and early career clinical scientists to apply data science techniques to medical decision support. The project will also include groups underrepresented in Science, Technology, Engineering, and Mathematics (STEM) by recruiting new students and integrating the research training into a highly diverse laboratory.The project will apply the emerging field of tropical geometry to soft computing methods. This approach will avoid the disadvantages of conventional soft computing paradigms such as fuzzy logic by: a) reducing the need for a large number of training examples, b) allowing smooth and fast optimization during model training, and c) enabling a systematic reduction in the size of the parameter space, thereby reducing the likelihood of overfitting the data. Approximate rules will be collected from a panel of cardiologists to determine candidacy for advanced therapies. Using the planned method, these rules will be incorporated into a model to predict the progression of HF and identify patients who are eligible for and most likely to benefit from heart transplantation or durable MCS devices. Optimized rules will be extracted from the trained model and verified by a panel of cardiologists for correctness and clinical utility. Moreover, the proposed machine learning paradigm will be able to generate novel and interpretable clinical rules that add to our understanding of how best to manage patients with advanced HF.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.