EAGER: Causal Analysis through Formal Reasoning and AI for Cancer Diagnostics

EAGER：通过形式推理和人工智能进行癌症诊断的因果分析

• 批准号: 2320050
• 负责人: Borzoo Bonakdarpour
• 金额: $ 24万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320050&HistoricalAwards=false
• 关键词: EAGER; Causal; Analysis; through; Formal

Scientific investigations have two purposes: (1) discovering previously unknown associations between a natural phenomenon, and (2) generating precise mechanistic explanations for how the phenomena are causally related. Among these two, explanation is the most critical to achieve global impact. Identifying the cause of natural phenomena not only enables us to predict their future occurrences, but also implies the means in which we may prevent or treat such events (e.g., the effect of genetic mutations on development of cancer). This is particularly true in the medical domain, where erroneous treatments can result in catastrophic consequences. Indeed, the medical domain mainly focuses on identifying correlations rather than causation. Such root-cause analysis and causality-based predictive modeling are critically needed for more accurate diagnosis and the timely selection of an appropriate type of therapy. The project's novelties and impact are designing techniques by combining automated formal reasoning and artificial intelligence (AI) to discover the causal relation between events to answer deep questions on real causes of certain medical conditions.The project builds a prominent infrastructure for collecting preliminary data and designing proof of concept techniques that demonstrate the viability of this project’s approach based on formal reasoning and AI to extract causal structures in health and medical domains. The project first investigates two different notions of causality (Halpern-Pearl and Granger) and explores their fitness in the medical domain. Then, the project reduces the problem of extracting causal structures to decision procedures that solve certain problems on automated reasoning. To this end, the project utilizes off-the-shelf decision procedures for solving the satisfiability problem for quantified Boolean formulas (QBF) and satisfiability modulo theory (SMT). In the probabilistic and predictive settings, the project incorporates model checkers for probabilistic systems to reason about models generated from medical data and Granger/probabilistic causality. Finally, in order to tackle the scalability issues in automated formal reasoning about causality, the project combines the developed techniques with AI, and augments AI with formal reasoning during the training phase.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.

科学研究具有两个目的：（1）发现自然现象之间以前未知的关联，（2）不幸的是，对现象的相关方式产生了精确的机械解释。在这两个中，解释对于实现全球影响是最关键的。确定自然现象的原因不仅使我们能够预测其未来发生的事件，而且还意味着我们可以预防或治疗此类事件的手段（例如，遗传突变对癌症发展的影响）。在医疗领域尤其如此，在医学领域中，错误的治疗可能导致灾难性后果。实际上，医学领域主要集中于识别相关性而不是原因。对于更准确的诊断和及时选择适当类型的治疗，需要至关重要的基于根的分析和基于偶然性的预测建模。该项目的新颖性和影响是通过结合自动化的正式推理和人工智能（AI）来设计技术的设计技术，以发现事件之间的可行关系，以回答有关某些医疗状况的真实原因的深刻问题。该项目建立了一个著名的基础设施，以收集基于该项目的概念构建的概念数据和设计的概念证明，从而表明对项目的构建进行了逐步构建，从而在进行了计划的范围内，以进行医学构建的范围，以提出医学范围内的方法和构建方法。该项目首先研究了两种不同的偶然性（Halpern-Pearl和Granger），并探讨了它们在医疗领域的适合度。然后，该项目减少了将因果结构提取到解决自动推理上某些问题的决策程序的问题。为此，该项目利用现成的决策程序来解决量化布尔公式（QBF）和满意度模式理论（SMT）的满意度问题。在有问题和预测性的设置中，该项目将模型检查器纳入了概率系统，以推理医疗数据和Granger/Probabilistic Castality产生的模型。最后，为了解决有关偶然性的自动正式推理中的可伸缩性问题，该项目将开发的技术与AI结合在一起，并在培训阶段将AI与正式推理相结合。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查的评估来通过评估来支持的珍贵。