CAREER: Developing Actionable Methods for Observational Health Data

职业：为观察健康数据开发可行的方法

• 批准号: 2145625
• 负责人: Ping Zhang
• 金额: $ 55.68万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145625&HistoricalAwards=false
• 关键词: CAREER; Developing; Actionable; Methods; Observational

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.

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，共病、处方和实验室结果），了解异质性患者及其在现实世界环境中对治疗的反应。许多现有的机器学习工作专注于对观测数据进行预测（例如，死亡的可能性或心脏病发作的风险）而不是向医生提供可行的建议（例如，什么时候给病人服用什么药）。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局的复杂混杂因素; 2）治疗建议的可解释性，以支持可操作的决策; 3）训练有素的模型可转移到不同的环境。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，为观察性健康数据开发可行的方法。研究团队将与医学研究人员和医生密切合作，就各种临床问题进行模型验证，并将积极寻求技术转让机会。该项目将为研究生和本科生提供新的计划，课程，研究和实习机会，用于医疗保健应用的机器学习。该项目将整合深度学习算法和因果推理技术，用于对纵向观测数据进行建模并调整混杂因素，并为观测数据开发可操作的方法，其中包括两个互补的任务：个别治疗效果（ITE），估计对特定目标采取特定行动的结果改善;和动态治疗方案（DTR），其基于不断发展的治疗和协变量历史导出一系列决策规则，每个干预阶段一个。在第一个推力中，研究小组将通过对观察性健康数据中的历史信息进行循环建模来对时变和隐藏的混杂因素进行建模，以估计ITE;研究人员将生成个性化的治疗时机建议，其中具有实现最佳因果效应的不确定性量化;他们将通过变量和全局视角提供治疗建议的可解释性。在第二个方面，研究小组将通过患者恢复和平衡权重来消除观察性健康数据中的混淆偏差;研究人员将开发一种用于DTR学习的去混淆强化学习模型，同时考虑短期和长期奖励;他们将引入一种策略适应方法到拟议的模型中，将学习到的DTR策略转移到新的源数据集。这个奖项反映了NSF的法定使命，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。

项目成果

DREAM: Domain Invariant and Contrastive Representation for Sleep Dynamics

• DOI: 10.1109/icdm54844.2022.00126
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Seungyeon Lee;Thai-Hoang Pham;Ping Zhang

Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis.

估计败血症的时间处理抗生素管理的治疗效果。

• DOI: 10.1038/s42256-023-00638-0
• 发表时间: 2023-04
• 期刊: NATURE MACHINE INTELLIGENCE
• 影响因子: 23.8
• 作者: Liu, Ruoqi;Hunold, Katherine M.;Caterino, Jeffrey M.;Zhang, Ping
• 通讯作者: Zhang, Ping

Fairness and Accuracy Under Domain Generalization

• DOI: 10.48550/arxiv.2301.13323
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: Thai-Hoang Pham;Xueru Zhang;Ping Zhang

Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes.

• DOI: 10.1145/3534678.3539413
• 发表时间: 2022-08
• 期刊: KDD : proceedings. International Conference on Knowledge Discovery & Data Mining