SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction Article Swipe

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Zhihao Yu , Xu Chu , Yujie Jin , Yasha Wang , Junfeng Zhao ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2405.09039

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess unnecessary details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via elaborated attentions and learns to impute missing values through a novel self-supervised pre-training approach that reconstructs missing data representations in the latent space. By adopting missing-aware attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2405.09039
PDF: https://arxiv.org/pdf/2405.09039
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4396986769

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DOI: https://doi.org/10.48550/arxiv.2405.09039

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Title: SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-05-15

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Authors: Zhihao Yu, Xu Chu, Yujie Jin, Yasha Wang, Junfeng Zhao

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Landing page: https://arxiv.org/abs/2405.09039

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PDF URL: https://arxiv.org/pdf/2405.09039

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Concepts: Missing data, Computer science, Artificial intelligence, Machine learning, Psychology

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Cited by: 0

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Related works (count): 10

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