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

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Title: SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome.
Authors: Zhang, Jielu1 (AUTHOR), Mu, Lan1 (AUTHOR) mulan@uga.edu, Zhang, Donglan2 (AUTHOR), Chen, Zhuo3 (AUTHOR), Rajbhandari-Thapa, Janani3 (AUTHOR), Pagán, José A.4 (AUTHOR), Li, Yan5 (AUTHOR), Mai, Gengchen6 (AUTHOR), Zhou, Zhongliang7 (AUTHOR)
Source: International Journal of Geographical Information Science. Aug2026, Vol. 40 Issue 8, p2537-2568. 32p.
Subjects: Cardiac arrest, Geospatial data, Disease risk factors, Deep learning, Counterfactuals (Logic), Prediction models, Medical geography, Prognostic models
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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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PubType: Academic Journal
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  Data: SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Jielu%22">Zhang, Jielu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mu%2C+Lan%22">Mu, Lan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mulan@uga.edu</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Donglan%22">Zhang, Donglan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhuo%22">Chen, Zhuo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rajbhandari-Thapa%2C+Janani%22">Rajbhandari-Thapa, Janani</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pagán%2C+José+A%2E%22">Pagán, José A.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yan%22">Li, Yan</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mai%2C+Gengchen%22">Mai, Gengchen</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Zhongliang%22">Zhou, Zhongliang</searchLink><relatesTo>7</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Aug2026, Vol. 40 Issue 8, p2537-2568. 32p.
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  Data: <searchLink fieldCode="DE" term="%22Cardiac+arrest%22">Cardiac arrest</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Counterfactuals+%28Logic%29%22">Counterfactuals (Logic)</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+geography%22">Medical geography</searchLink><br /><searchLink fieldCode="DE" term="%22Prognostic+models%22">Prognostic models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/13658816.2024.2443757
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      – Code: eng
        Text: English
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        PageCount: 32
        StartPage: 2537
    Subjects:
      – SubjectFull: Cardiac arrest
        Type: general
      – SubjectFull: Geospatial data
        Type: general
      – SubjectFull: Disease risk factors
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Counterfactuals (Logic)
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Medical geography
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      – SubjectFull: Prognostic models
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      – TitleFull: SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome.
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            – D: 01
              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
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