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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration

February 1, 2023

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Authors

Liantao Ma

Peking University


Junyi Gao

Key Laboratory of High Confidence Software Technologies


Yasha Wang

Peking University


Chaohe Zhang

Peking University


Jiangtao Wang

Lancaster University


Wenjie Ruan

Lancaster University


Wen Tang

Peking University Third Hospital


Xin Gao

Peking University


Xinyu Ma

Peking University


DOI:

10.1609/aaai.v34i01.5427


Abstract:

Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performance, but there are still several major issues that have not been fully taken into consideration. First, the historical variation pattern of the biomarker in diverse time scales plays a vital role in indicating the health status, but it has not been explicitly extracted by existing works. Second, key factors that strongly indicate the health risk are different among patients. It is still challenging to adaptively make use of the features for patients in diverse conditions. Third, using prediction models as the black box will limit the reliability in clinical practice. However, none of the existing works can provide satisfying interpretability and meanwhile achieve high prediction performance. In this work, we develop a general health status representation learning model, named AdaCare. It can capture the long and short-term variations of biomarkers as clinical features to depict the health status in multiple time scales. It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability. We conduct a health risk prediction experiment on two real-world datasets. Experiment results indicate that AdaCare outperforms state-of-the-art approaches and provides effective interpretability, which is verifiable by clinical experts.

Topics: AAAI

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HOW TO CITE:

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration Proceedings of the AAAI Conference on Artificial Intelligence (2020) 825-832.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration AAAI 2020, 825-832.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma (2020). AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration. Proceedings of the AAAI Conference on Artificial Intelligence, 825-832.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma. AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.825-832.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma. 2020. AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration. "Proceedings of the AAAI Conference on Artificial Intelligence". 825-832.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma. (2020) "AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration", Proceedings of the AAAI Conference on Artificial Intelligence, p.825-832

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma, "AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration", AAAI, p.825-832, 2020.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma. "AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.825-832.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma. "AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 825-832.

Liantao Ma||Junyi Gao||Yasha Wang||Chaohe Zhang||Jiangtao Wang||Wenjie Ruan||Wen Tang||Xin Gao||Xinyu Ma. AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration. AAAI[Internet]. 2020[cited 2023]; 825-832.


ISSN: 2374-3468


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