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.

每年，美国在医疗保健方面的支出占国内生产总值（GDP）的近20%，而增长继续受到老年人口进入医疗保险的推动。虽然费用巨大，但许多患者未能得到及时有效的药物治疗。准确的诊断在临床决策中至关重要。然而，“预防胜于治疗”，因为预防和早期干预可以防止老年人遭受更多的疾病和/或更广泛的治疗。此外，当大量患者处于进展性疾病的晚期时，建立预测模型为时已晚，这严重损害了他们的健康。同时，为了便于医疗服务提供者使用，预测模型需要具有可解释性和可信赖性。此外，人类利益相关者之间的有效互动（例如，开发人员、领域专家和/或最终用户）和清晰的模型解释不仅提高了模型性能，而且增强了人们的信任。该研究项目旨在开发算法和方法，以支持实施值得信赖和时间效率高的数据驱动的预防和早期干预决策。该项目提出的主要方法是交互式可解释的深度生存分析。生存分析旨在预测关注事件的时间，这在医疗保健中非常有益，可用于建模疾病进展，识别预后因素，评估健康风险。该项目将在健康老龄化和精准医学领域建立深度生存分析模型，以支持临床决策，特别是在许多患者患有该疾病之前的疾病进展早期阶段。深度生存分析是一种“黑箱”模型，利益相关者无法告诉模型是如何运作的，以及它是如何产生决策的，因此限制了它在实践中的使用。该项目将开发在深度生存分析模型中实现透明度和可信度的方法，并对领域知识和专家反馈进行编码，以实现更好的预测性能。更具体地说，该项目将提出一个时间依赖的反事实梯度积分，以解释是什么使模型输出在每个时间间隔与反事实生存状态区分开来。该项目还将把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。受人在环的启发，该项目将进一步研究有效的方案，以数学方式制定医生的定性反馈，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。