SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome.

IF 5.1 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jielu Zhang, Lan Mu, Donglan Zhang, Zhuo Chen, Janani Rajbhandari-Thapa, José A Pagán, Yan Li, Gengchen Mai, Zhongliang Zhou
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引用次数: 0

Abstract

Understanding the relationship between risk factors, geospatial patterns, and disease outcomes is essential in health geography research. These relationships can inform the implementation of healthcare and public health strategies to improve health outcomes. To accurately uncover such complex relationships, it is necessary to have a predictive model capable of integrating both health variables and spatial information to forecast health outcomes, along with a tool to interpret and reveal the patterns identified by this model. We developed a Spatial Counterfactual Explainable Deep Learning model (SpaCE), comprising a spatially explicit health outcome predictor and a prototype-guided counterfactual explanation. The SpaCE model unifies geospatial and health variables to improve predictions and generates hypothetical examples with minimal changes but opposite outcomes. Using these counterfactuals, SpaCE assesses the impact of each variable in different spatial contexts. We evaluated the model for predicting cardiac arrest survival outcomes. With a 0.682 AUCROC score, the SpaCE exceeds baseline models by 10.2%. Further analysis also reveals that the geospatial context significantly affects how various risk factors affect the survival outcomes of patients. Overall, the SpaCE model significantly improves predictive accuracy and explainability. It provides targeted interventions at both individual and geographic levels, and the cardiac arrest case study shows its high adaptability to various disease scenarios.

空间:用于预测院外心脏骤停生存结果的空间反事实可解释深度学习模型。
了解风险因素、地理空间格局和疾病结果之间的关系在健康地理学研究中至关重要。这些关系可以为卫生保健和公共卫生战略的实施提供信息,以改善健康结果。为了准确地揭示这种复杂的关系,需要有一个能够整合健康变量和空间信息来预测健康结果的预测模型,以及一个解释和揭示该模型确定的模式的工具。我们开发了一个空间反事实可解释深度学习模型(SpaCE),包括一个空间明确的健康结果预测器和一个原型引导的反事实解释。空间模型将地理空间和健康变量统一起来,以改进预测,并生成变化最小但结果相反的假设示例。利用这些反事实,SpaCE评估了每个变量在不同空间环境中的影响。我们评估了预测心脏骤停生存结果的模型。AUCROC得分为0.682,SpaCE超过基线模型10.2%。进一步的分析还表明,地理空间环境显著影响各种危险因素如何影响患者的生存结果。总体而言,SpaCE模型显著提高了预测精度和可解释性。它在个人和地理层面提供有针对性的干预措施,心脏骤停案例研究显示其对各种疾病情景的高度适应性。
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来源期刊
CiteScore
11.00
自引率
7.00%
发文量
81
审稿时长
9 months
期刊介绍: International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.
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