@misc{10481/110032,
year = {2025},
url = {https://hdl.handle.net/10481/110032},
abstract = {This study addresses the challenge of interpreting complex continuous glucose monitoring data in diabetes management by proposing a domain-guided fine-tuning methodology for Large Language Models. Using expert-modeled fuzzy logic datasets and task-aware prompt engineering, the approach enables LLMs to generate accurate, concise, and clinically meaningful summaries from raw glucose data. Experimental results show that fine-tuned GPT-4o achieves superior performance, demonstrating the potential of expert-aligned language models to support medical decision-making and reduce the burden on healthcare systems.},
organization = {MICIU/AEI/10.13039/501100011033, TPID2021-127275OB-I00, PID2021-126363NB-I00 and PDC2023-145863-I00},
organization = {European Union NextGenerationEU/PRTR},
organization = {Universidad de Jaén/ CBUA},
publisher = {Elsevier},
keywords = {Diabetes management},
keywords = {Prompt engineering},
keywords = {Continuous glucose monitoring},
title = {A novel fine-tuning and evaluation methodology for large language models on IoT raw data summaries (LLM-RawDMeth): A joint perspective in diabetes care},
doi = {10.1016/j.cmpb.2025.108878},
author = {Gaitán-Guerrero, Juan F. and Martínez Cruz, Carmen and Espinilla, Macarena and Díaz-Jiménez, David and López, Jose L.},
}