@inproceedings{fadlon-etal-2025-decode,
title = "Decode Like a Clinician: Enhancing {LLM} Fine-Tuning with Temporal Structured Data Representation",
author = "Fadlon, Daniel and
Dov, David and
Bennett, Aviya and
Heller-Miron, Daphna and
Levy, Gad and
Bar, Kfir and
Weiss-Meilik, Ahuva",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.103/",
pages = "1906--1922",
ISBN = "979-8-89176-298-5",
abstract = "Predictive modeling of hospital patient data is challenging due to its structured format, irregular timing of measurements, and variation in data representation across institutions. While traditional models often struggle with such inconsistencies, Large Language Models (LLMs) offer a flexible alternative. In this work, we propose a method for verbalizing structured Electronic Health Records (EHRs) into a format suitable for LLMs and systematically examine how to include time-stamped clinical observations{---}such as lab tests and vital signs{---}from previous time points in the prompt. We study how different ways of structuring this temporal information affect predictive performance, and whether fine-tuning alone enables LLMs to effectively reason over such data. Evaluated on two real-world hospital datasets and MIMIC-IV, our approach achieves strong in-hospital and cross-hospital performance, laying the groundwork for more generalizable clinical modeling."
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<abstract>Predictive modeling of hospital patient data is challenging due to its structured format, irregular timing of measurements, and variation in data representation across institutions. While traditional models often struggle with such inconsistencies, Large Language Models (LLMs) offer a flexible alternative. In this work, we propose a method for verbalizing structured Electronic Health Records (EHRs) into a format suitable for LLMs and systematically examine how to include time-stamped clinical observations—such as lab tests and vital signs—from previous time points in the prompt. We study how different ways of structuring this temporal information affect predictive performance, and whether fine-tuning alone enables LLMs to effectively reason over such data. Evaluated on two real-world hospital datasets and MIMIC-IV, our approach achieves strong in-hospital and cross-hospital performance, laying the groundwork for more generalizable clinical modeling.</abstract>
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%0 Conference Proceedings
%T Decode Like a Clinician: Enhancing LLM Fine-Tuning with Temporal Structured Data Representation
%A Fadlon, Daniel
%A Dov, David
%A Bennett, Aviya
%A Heller-Miron, Daphna
%A Levy, Gad
%A Bar, Kfir
%A Weiss-Meilik, Ahuva
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F fadlon-etal-2025-decode
%X Predictive modeling of hospital patient data is challenging due to its structured format, irregular timing of measurements, and variation in data representation across institutions. While traditional models often struggle with such inconsistencies, Large Language Models (LLMs) offer a flexible alternative. In this work, we propose a method for verbalizing structured Electronic Health Records (EHRs) into a format suitable for LLMs and systematically examine how to include time-stamped clinical observations—such as lab tests and vital signs—from previous time points in the prompt. We study how different ways of structuring this temporal information affect predictive performance, and whether fine-tuning alone enables LLMs to effectively reason over such data. Evaluated on two real-world hospital datasets and MIMIC-IV, our approach achieves strong in-hospital and cross-hospital performance, laying the groundwork for more generalizable clinical modeling.
%U https://aclanthology.org/2025.ijcnlp-long.103/
%P 1906-1922
Markdown (Informal)
[Decode Like a Clinician: Enhancing LLM Fine-Tuning with Temporal Structured Data Representation](https://aclanthology.org/2025.ijcnlp-long.103/) (Fadlon et al., IJCNLP-AACL 2025)
ACL
- Daniel Fadlon, David Dov, Aviya Bennett, Daphna Heller-Miron, Gad Levy, Kfir Bar, and Ahuva Weiss-Meilik. 2025. Decode Like a Clinician: Enhancing LLM Fine-Tuning with Temporal Structured Data Representation. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1906–1922, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.