CRII:SCH: Interactive Explainable Deep Survival Analysis

CRII:SC​​H：交互式可解释深度生存分析

• 批准号: 2245739
• 负责人: Lu Wang
• 金额: $ 17.5万
• 依托单位: Texas State University - San Marcos
• 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: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245739&HistoricalAwards=false
• 关键词: CRII; SCH; Interactive; Explainable; Deep

Annually, the United States spends almost 20% of gross domestic product (GDP) in healthcare with growth continued to be boosted by a greying population aging into Medicare. Although the cost is huge, numerous patients fail to get timely and effective medication cure. Accurate diagnosis is critical in clinical decision making. However, “prevention is better than cure” as prevention and early intervention will prevent the aging people from suffering more diseases and/or more extensive treatments. Also, it is too late to build the prediction model when a lot of patients have been observed in the late stage of a progressive disease, which severely damages their health. Meanwhile, in order to be usable by healthcare providers, the prediction model needs to be interpretable and trustable. Also, efficient interaction between human stakeholders (e.g., developers, domain experts and/or end-users) and clear model interpretation not only improve the model performance but also enhance human trust. The proposed research project aims at developing algorithms and methods that support implementation of trustworthy and time-efficient data-driven decision making for prevention and early intervention.The main approach proposed in this project is interactive explainable deep survival analysis. Survival analysis aims at predicting the time to event of interest, which is extremely beneficial in healthcare for modeling disease progression, identifying prognostic factors, assessing risk of health. This project will build deep survival analysis models in healthy aging and precision medicine to support clinical decision making, especially in the early stage of a progressive disease before a lot of patients have been suffered from that disease. Deep survival analysis is a kind of “black box” model that stakeholders cannot tell how the model operates and how it comes to its decisions and hence limits its usage in practice. This project will develop methods to achieve both transparency and trustworthiness in deep survival analysis models with encoding of domain knowledge and expert feedback to achieve better prediction performance. More specifically, this project will propose a time-dependent counterfactual gradient integration to interpret what makes the model output differentiate from the counterfactual survival status at each time interval. This project will also incorporate feature attribution priors into the training process of deep survival analysis model to improve consistency of the explanation as well as the performance and trustworthiness of the model. Inspired by human-in-the-loop, this project will further investigate efficient schemes to mathematically formulate physicians' qualitative feedback, and interactively incorporate them in the learning process of the model with powerful perceptual user interface to efficiently encode diverse types of feedback from physicians.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.

美国每年将近20%的国内生产总值（GDP）用于医疗保健，老龄化人口加入医疗保险继续推动增长，尽管成本巨大，但许多患者未能得到及时有效的药物治疗。然而，“预防胜于治疗”，因为预防和早期干预将防止老年人遭受更多疾病和/或更广泛的治疗。此外，现在建立预测模型为时已晚。很多患者已经在疾病进展的晚期阶段观察到这种情况，这会严重损害他们的健康。同时，为了可供医疗保健提供者使用，预测模型需要可解释且可信。此外，人类利益相关者（例如开发人员、领域）之间的有效互动。专家和/或最终用户）和清晰的模型解释不仅可以提高模型性能，还可以增强人类信任。拟议的研究项目旨在开发支持实施可信且高效的数据驱动决策的算法和方法。预防和早期干预。本文提出的主要方法该项目是交互式可解释的深度生存分析，旨在预测感兴趣事件的发生时间，这对于疾病进展建模、识别预后因素、评估健康风险非常有益。该项目将建立健康的深度生存分析模型。衰老和精准医学支持临床决策，特别是在疾病进展的早期阶段，在许多患者患有该疾病之前，深度生存分析是一种“黑匣子”模型，利益相关者无法分辨该模型的原理。运作以及如何做出决定，从而限制了其使用在实践中，该项目将开发方法，通过领域知识和专家反馈的编码来实现深度生存分析模型的透明度和可信度，以实现更好的预测性能。更具体地说，该项目将提出一种依赖于时间的反事实梯度积分来解释什么。使模型输出与每个时间间隔的反事实生存状态区分开来。该项目还将特征归因先验纳入深度生存分析模型的训练过程中，以提高解释的一致性以及模型的性能和可信度。经过在人机循环中，该项目将研究以数学方式制定医生的定性反馈的方案，并通过强大的感知用户界面将它们交互式地纳入模型的学习过程中，以有效地编码来自医生的高效不同类型的反馈。该奖项NSF 的法定使命并通过评估被认为值得支持，这反映了使用基金会的智力优点和更广泛的影响审查标准。