FAI: An Interpretable AI Framework for Care of Critically Ill Patients Involving Matching and Decision Trees

FAI：用于危重患者护理的可解释人工智能框架，涉及匹配和决策树

• 批准号: 2147061
• 负责人: Cynthia Rudin
• 金额: $ 62.5万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147061&HistoricalAwards=false
• 关键词: FAI; Interpretable; AI; Framework; Care

This project introduces a framework for interpretable, patient-centered causal inference and policy design for in-hospital patient care. This framework arose from a challenging problem, which is how to treat critically ill patients who are at risk for seizures (subclinical seizures) that can severely damage a patient's brain. In this high-stakes application of artificial intelligence, the data are complex, including noisy time-series, medical history, and demographic information. The goal is to produce interpretable causal estimates and policy decisions, allowing doctors to understand exactly how data were combined, permitting better troubleshooting, uncertainty quantification, and ultimately, trust. The core of the project's framework consists of novel and sophisticated matching techniques, which match each treated patient in the dataset with other (similar) patients who were not treated. Matching emulates a randomized controlled trial, allowing the effect of the treatment to be estimated for each patient, based on the outcomes from their matched group. A second important element of the framework involves interpretable policy design, where sparse decision trees will be used to identify interpretable subgroups of individuals who should receive similar treatments.The matching techniques developed in this project will be within the new family of "almost-matching-exactly" (AME) techniques. AME techniques use machine learning on a training set to determine how to construct high-quality matched groups. In applying AME techniques to analyze seizure risk and treatment for critically ill patients, there are two major challenges that this project addresses: how to incorporate mechanistic models for drug absorption, and how to perform uncertainty quantification. Importantly, the project also addresses the release of AME code in several formats to be used by non-experts. The policy design aspect of the project involves the optimization of sparse decision trees. This project involves a close collaboration between experts in machine learning, causal inference, databases, and neurology, with the goal to improve patient care in high-stakes hospital settings where experiments cannot be conducted, and the only way to assess causal effects is through the analysis of observational data.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.

该项目介绍了一个可解释的、以患者为中心的因果推理和住院患者护理政策设计框架。这个框架源于一个具有挑战性的问题，即如何治疗有癫痫发作风险的危重患者（亚临床癫痫发作），这可能严重损害患者的大脑。在这种高风险的人工智能应用中，数据很复杂，包括嘈杂的时间序列、病史和人口统计信息。目标是产生可解释的因果估计和政策决策，使医生能够准确地理解数据是如何组合的，从而更好地排除故障，量化不确定性，并最终获得信任。该项目的核心框架由新颖而复杂的匹配技术组成，该技术将数据集中每个接受治疗的患者与其他未接受治疗的（类似）患者进行匹配。匹配模拟了随机对照试验，允许根据匹配组的结果估计每个患者的治疗效果。该框架的第二个重要元素涉及可解释的政策设计，其中稀疏的决策树将用于识别应该接受类似处理的可解释的个人子组。本项目开发的匹配技术将属于“几乎完全匹配”（AME）技术的新家族。AME技术在训练集上使用机器学习来确定如何构建高质量的匹配组。在应用AME技术分析危重患者的癫痫发作风险和治疗时，本项目面临两个主要挑战：如何纳入药物吸收的机制模型，以及如何进行不确定度量化。重要的是，该项目还解决了以非专家使用的几种格式发布AME代码的问题。项目的策略设计方面涉及到稀疏决策树的优化。该项目涉及机器学习、因果推理、数据库和神经学专家之间的密切合作，目标是改善高风险医院环境中无法进行实验的患者护理，评估因果效应的唯一方法是通过分析观察数据。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。

项目成果

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data

用于分析可穿戴设备、传感器和分布式数据的可解释因果推理

• 发表时间: 2024
• 期刊: Proceedings of the International Conference on Artificial Intelligence and Statistics
• 作者: Srikar Katta, Harsh Parikh

From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference

从特征重要性到距离度量：因果推理的几乎精确匹配方法

• 发表时间: 2023
• 期刊: Uncertainty in artificial intelligence
• 作者: Quinn Lanners, Harsh Parikh

Safe and Interpretable Estimation of Optimal Treatment Regimes

最佳治疗方案的安全且可解释的估计

• 发表时间: 2024
• 期刊: Proceedings of the International Conference on Artificial Intelligence and Statistics
• 作者: Harsh Parikh, Quinn Lanners

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design

用于最佳治疗方案和最佳政策设计的加权稀疏决策树的快速优化

• 发表时间: 2022
• 期刊: 2022.
• 作者: Ali Behrouz, Mathias Lecuyer

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance

• DOI: 10.48550/arxiv.2309.13775
• 发表时间: 2023-09
• 期刊: ArXiv
• 作者: J. Donnelly;Srikar Katta;C. Rudin;E. Browne