@inproceedings{jana-etal-2026-disgraph,
title = "{D}is{G}raph-{RP}: Graph-Augmented Temporal Modeling with Aspect-Based Contrastive Encoding of Discharge Summary for Readmission Prediction",
author = "Jana, Sudeshna and
Dasgupta, Tirthankar and
Sinha, Manjira and
Mitra, Pabitra",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.59/",
pages = "801--812",
ISBN = "979-8-89176-384-5",
abstract = "Predicting hospital readmissions is a critical clinical task with substantial implications for patient outcomes and healthcare cost management. We propose DisGraph-RP, a graph-augmented temporal modeling framework that integrates structured discourse-aware text representation with cross-admission relational reasoning. Our approach introduces a Section-Aware Contrastive Encoder that leverages section segmentation and aspect-based supervision to produce fine-grained representations of discharge summaries. These representations are then composed over time using a Graph-Based temporal module that encodes inter-visit dependencies through learned edge relations, enabling the model to capture disease progression, treatment history, and recurrent risk signals. Experiments on multiple real-world datasets demonstrate that DisGraph-RP achieves significant improvements over strong baselines, including transformer-based clinical models and prompting-based LLM approaches. Our findings highlight the importance of combining discourse-informed text encoding with temporal graph reasoning for robust clinical outcome prediction."
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<abstract>Predicting hospital readmissions is a critical clinical task with substantial implications for patient outcomes and healthcare cost management. We propose DisGraph-RP, a graph-augmented temporal modeling framework that integrates structured discourse-aware text representation with cross-admission relational reasoning. Our approach introduces a Section-Aware Contrastive Encoder that leverages section segmentation and aspect-based supervision to produce fine-grained representations of discharge summaries. These representations are then composed over time using a Graph-Based temporal module that encodes inter-visit dependencies through learned edge relations, enabling the model to capture disease progression, treatment history, and recurrent risk signals. Experiments on multiple real-world datasets demonstrate that DisGraph-RP achieves significant improvements over strong baselines, including transformer-based clinical models and prompting-based LLM approaches. Our findings highlight the importance of combining discourse-informed text encoding with temporal graph reasoning for robust clinical outcome prediction.</abstract>
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%0 Conference Proceedings
%T DisGraph-RP: Graph-Augmented Temporal Modeling with Aspect-Based Contrastive Encoding of Discharge Summary for Readmission Prediction
%A Jana, Sudeshna
%A Dasgupta, Tirthankar
%A Sinha, Manjira
%A Mitra, Pabitra
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F jana-etal-2026-disgraph
%X Predicting hospital readmissions is a critical clinical task with substantial implications for patient outcomes and healthcare cost management. We propose DisGraph-RP, a graph-augmented temporal modeling framework that integrates structured discourse-aware text representation with cross-admission relational reasoning. Our approach introduces a Section-Aware Contrastive Encoder that leverages section segmentation and aspect-based supervision to produce fine-grained representations of discharge summaries. These representations are then composed over time using a Graph-Based temporal module that encodes inter-visit dependencies through learned edge relations, enabling the model to capture disease progression, treatment history, and recurrent risk signals. Experiments on multiple real-world datasets demonstrate that DisGraph-RP achieves significant improvements over strong baselines, including transformer-based clinical models and prompting-based LLM approaches. Our findings highlight the importance of combining discourse-informed text encoding with temporal graph reasoning for robust clinical outcome prediction.
%U https://aclanthology.org/2026.eacl-industry.59/
%P 801-812
Markdown (Informal)
[DisGraph-RP: Graph-Augmented Temporal Modeling with Aspect-Based Contrastive Encoding of Discharge Summary for Readmission Prediction](https://aclanthology.org/2026.eacl-industry.59/) (Jana et al., EACL 2026)
ACL