CAREER: Developing Actionable Methods for Observational Health Data

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。观察性健康数据包含大量临床信息（例如，合并症、处方和实验室结果），涉及异质性患者及其在现实环境中对治疗的反应。许多现有的机器学习工作侧重于对观察数据进行预测（例如，死亡几率或心脏病发作风险），而不是向医生提供可操作的建议（例如，何时为特定患者使用哪种药物）。为观察数据开发可操作的模型在三个方面具有挑战性：1)影响治疗分配和疾病进展结果的复杂混杂因素；2)可操作性决策支持治疗建议的可解释性；3)训练有素的模型在不同环境中的可移植性。为了应对这些挑战，该项目将整合深度学习算法和因果推理技术，为观察健康数据开发可操作的方法。研究团队将与医学研究人员和医生密切合作，对各种临床问题进行模型验证，并积极寻求技术转移机会。该项目将为研究生和本科生提供医疗保健应用机器学习的新项目、课程、研究和实习机会。该项目还将积极纳入代表性不足的学生，并扩大到高中和普通公众。该项目将整合深度学习算法和因果推理技术，用于纵向观测数据建模和调整混杂因素，并开发具有两个互补任务的观测数据的可操作方法：个体治疗效果（ITEs），用于估计对特定目标采取特定行动的结果的改善；动态治疗方案（DTRs），根据治疗的演变和协变量历史，得出一系列决策规则，每个干预阶段一个。在第一个重点中，研究小组将通过反复建模观察健康数据中的历史信息来估计ITEs，从而对时变和隐藏的混杂因素进行建模；研究人员将产生一个个性化的治疗时机推荐，不确定性量化，以达到最佳的因果效应；他们将通过变量和全局视角提供治疗建议的可解释性。在第二个重点中，研究小组将通过患者重采样和平衡权重来消除观察性健康数据中的混杂偏倚；研究人员将开发DTR学习的反建立强化学习模型，该模型同时考虑短期和长期奖励；他们将在提出的模型中引入策略自适应方法，将学习到的DTR策略转移到新的源数据集。该项目将导致向更广泛的机器学习和医疗保健社区传播用于不规则间隔时间序列的新方法和软件。该奖项反映了美国国家科学基金会的法定使命，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 award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Observational health data contain large amounts of clinical information (e.g., comorbidities, prescriptions, and laboratory results) about heterogeneous patients and their responses to treatments in real-world settings. Many existing machine learning works focus on making predictions on observational data (e.g., chance of death or risk of heart attack) instead of providing actionable suggestions to physicians (e.g., when to use which drug for a specific patient). Developing actionable models for observational data is challenging in three aspects: 1) complex confounding factors that infect both treatment assignments and disease progression outcomes; 2) interpretability of treatment recommendation for actionable decision support; and 3) transferability of well-trained models to different environments. To address these challenges, the project will integrate deep learning algorithms and causal inference techniques to develop actionable methods for observational health data. The research team will closely collaborate with medical researchers and physicians for model validation on various clinical problems, and will actively seek technology transfer opportunities. The project will provide graduate and undergraduate students with new programs, courses, research, and internship opportunities on machine learning for healthcare applications. The project will also actively include underrepresented students and outreach to high schools and the general public.The project will integrate deep learning algorithms and causal inference techniques for modeling longitudinal observational data and adjusting confounding factors, and develop actionable methods for observational data with two complementary tasks: individual treatment effects (ITEs), which estimate improvement in the outcome of taking a particular action to a particular target; and dynamic treatment regimes (DTRs), which derive a sequence of decision rules, one per stage of intervention, based on evolving treatment and covariate history. In the first thrust, the research team will model time-varying and hidden confounders by recurrently modeling historical information in observational health data for estimating ITEs; the researchers will generate a personalized treatment timing recommendation with an uncertainty quantification that achieves optimal causal effects; they will provide interpretability of treatment recommendations through both variable and global perspectives. In the second thrust, the research team will remove the confounding bias in observational health data via patient resampling and balancing weights; the researchers will develop a deconfounding reinforcement learning model for DTR learning, which simultaneously considers short-term and long-term rewards; they will introduce a policy adaptation method to the proposed model to transfer the learned DTR policies to new-source datasets. The project will result in the dissemination of new methods and software for irregularly spaced time series to the broader machine learning and healthcare communities.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.