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        <title>Resolution-Alignment-Completion of Tabular Electronic Health Records via Meta-Path Generative Sampling</title>
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    <abstract>The increasing availability of electronic health records (EHR) offers significant opportunities in data-driven healthcare, yet much of this data remains fragmented, semantically inconsistent, or incomplete. These issues are particularly evident in tabular patient records where important contextual information are lacking from the input for effective modeling. In this work, we introduce a system that performs ontology-based entity alignment to resolve and complete tabular data used in real-world clinical units. We transform patient records into a knowledge graph and capture its hidden structures through graph embeddings. We further propose a meta-path sample generation approach for completing the missing information. Our experiments demonstrate the system’s ability to augment cardiovascular disease (CVD) data for lab event detection, diagnosis prediction, and drug recommendation, enabling more robust and precise predictive models in clinical decision-making.</abstract>
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%0 Conference Proceedings
%T Resolution-Alignment-Completion of Tabular Electronic Health Records via Meta-Path Generative Sampling
%A Mehryar, S.
%Y Chang, Shuaichen
%Y Hulsebos, Madelon
%Y Liu, Qian
%Y Chen, Wenhu
%Y Sun, Huan
%S Proceedings of the 4th Table Representation Learning Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-268-8
%F mehryar-2025-resolution
%X The increasing availability of electronic health records (EHR) offers significant opportunities in data-driven healthcare, yet much of this data remains fragmented, semantically inconsistent, or incomplete. These issues are particularly evident in tabular patient records where important contextual information are lacking from the input for effective modeling. In this work, we introduce a system that performs ontology-based entity alignment to resolve and complete tabular data used in real-world clinical units. We transform patient records into a knowledge graph and capture its hidden structures through graph embeddings. We further propose a meta-path sample generation approach for completing the missing information. Our experiments demonstrate the system’s ability to augment cardiovascular disease (CVD) data for lab event detection, diagnosis prediction, and drug recommendation, enabling more robust and precise predictive models in clinical decision-making.
%R 10.18653/v1/2025.trl-1.17
%U https://aclanthology.org/2025.trl-1.17/
%U https://doi.org/10.18653/v1/2025.trl-1.17
%P 200-207

Markdown (Informal)

[Resolution-Alignment-Completion of Tabular Electronic Health Records via Meta-Path Generative Sampling](https://aclanthology.org/2025.trl-1.17/) (Mehryar, TRL 2025)

• Resolution-Alignment-Completion of Tabular Electronic Health Records via Meta-Path Generative Sampling (Mehryar, TRL 2025)

ACL

• S Mehryar. 2025. Resolution-Alignment-Completion of Tabular Electronic Health Records via Meta-Path Generative Sampling. In Proceedings of the 4th Table Representation Learning Workshop, pages 200–207, Vienna, Austria. Association for Computational Linguistics.