Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for Electronic Health Records

Jesus Lovon-Melgarejo, Thouria Ben-Haddi, Jules Di Scala, Jose G. Moreno, Lynda Tamine


Abstract
The lack of standardized evaluation benchmarks in the medical domain for text inputs can be a barrier to widely adopting and leveraging the potential of natural language models for health-related downstream tasks. This paper revisited an openly available MIMIC-IV benchmark for electronic health records (EHRs) to address this issue. First, we integrate the MIMIC-IV data within the Hugging Face datasets library to allow an easy share and use of this collection. Second, we investigate the application of templates to convert EHR tabular data to text. Experiments using fine-tuned and zero-shot LLMs on the mortality of patients task show that fine-tuned text-based models are competitive against robust tabular classifiers. In contrast, zero-shot LLMs struggle to leverage EHR representations. This study underlines the potential of text-based approaches in the medical field and highlights areas for further improvement.
Anthology ID:
2024.cl4health-1.23
Volume:
Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Dina Demner-Fushman, Sophia Ananiadou, Paul Thompson, Brian Ondov
Venues:
CL4Health | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
189–196
Language:
URL:
https://aclanthology.org/2024.cl4health-1.23
DOI:
Bibkey:
Cite (ACL):
Jesus Lovon-Melgarejo, Thouria Ben-Haddi, Jules Di Scala, Jose G. Moreno, and Lynda Tamine. 2024. Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for Electronic Health Records. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, pages 189–196, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for Electronic Health Records (Lovon-Melgarejo et al., CL4Health-WS 2024)
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PDF:
https://aclanthology.org/2024.cl4health-1.23.pdf