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

本计画介绍一个架构，以病人为中心的因果推论与住院病人照护政策设计。这一框架源于一个具有挑战性的问题，即如何治疗有癫痫发作（亚临床癫痫发作）风险的重症患者，这些癫痫发作可能严重损害患者的大脑。在人工智能的这种高风险应用中，数据是复杂的，包括嘈杂的时间序列、病史和人口统计信息。其目标是产生可解释的因果估计和政策决策，使医生能够准确地了解数据是如何组合的，从而更好地排除故障，量化不确定性，并最终建立信任。该项目框架的核心由新颖而复杂的匹配技术组成，该技术将数据集中的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。框架的第二个重要组成部分涉及可解释的政策设计，稀疏决策树将被用来确定可解释的个人谁应该接受类似treatment.The匹配技术在这个项目中开发的亚组将在新的家庭“几乎匹配，完全”（AME）技术。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技术分析重症患者的癫痫发作风险和治疗时，该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性量化。重要的是，该项目还解决了以几种格式发布AME代码供非专家使用的问题。该项目的政策设计方面涉及稀疏决策树的优化。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，旨在改善无法进行实验的高风险医院环境中的患者护理，评估因果效应的唯一方法是通过对观测数据的分析。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.