@inproceedings{mitrovic-etal-2025-preliminary,
title = "A Preliminary Study on {NLP}-Based Personalized Support for Type 1 Diabetes Management",
author = "Mitrovi{\'c}, Sandra and
Fontana, Federico and
Zignoli, Andrea and
Mattioni Maturana, Felipe and
Berchtold, Christian and
Malpetti, Daniele and
Scott, Sam and
Azzimonti, Laura",
editor = "Ananiadou, Sophia and
Demner-Fushman, Dina and
Gupta, Deepak and
Thompson, Paul",
booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.cl4health-1.25/",
doi = "10.18653/v1/2025.cl4health-1.25",
pages = "298--302",
ISBN = "979-8-89176-238-1",
abstract = "The proliferation of wearable devices and sports monitoring apps has made tracking physical activity more accessible than ever. For individuals with Type 1 diabetes, regular exercise is essential for managing the condition, making personalized feedback particularly valuable. By leveraging data from physical activity sessions, NLP-generated messages can offer tailored guidance to help users optimize their workouts and make informed decisions. In this study, we assess several open-source pre-trained NLP models for this purpose. Contrary to expectations, our findings reveal that models fine-tuned on medical data or excelling in medical benchmarks do not necessarily produce high-quality messages."
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<abstract>The proliferation of wearable devices and sports monitoring apps has made tracking physical activity more accessible than ever. For individuals with Type 1 diabetes, regular exercise is essential for managing the condition, making personalized feedback particularly valuable. By leveraging data from physical activity sessions, NLP-generated messages can offer tailored guidance to help users optimize their workouts and make informed decisions. In this study, we assess several open-source pre-trained NLP models for this purpose. Contrary to expectations, our findings reveal that models fine-tuned on medical data or excelling in medical benchmarks do not necessarily produce high-quality messages.</abstract>
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%0 Conference Proceedings
%T A Preliminary Study on NLP-Based Personalized Support for Type 1 Diabetes Management
%A Mitrović, Sandra
%A Fontana, Federico
%A Zignoli, Andrea
%A Mattioni Maturana, Felipe
%A Berchtold, Christian
%A Malpetti, Daniele
%A Scott, Sam
%A Azzimonti, Laura
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%Y Gupta, Deepak
%Y Thompson, Paul
%S Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-238-1
%F mitrovic-etal-2025-preliminary
%X The proliferation of wearable devices and sports monitoring apps has made tracking physical activity more accessible than ever. For individuals with Type 1 diabetes, regular exercise is essential for managing the condition, making personalized feedback particularly valuable. By leveraging data from physical activity sessions, NLP-generated messages can offer tailored guidance to help users optimize their workouts and make informed decisions. In this study, we assess several open-source pre-trained NLP models for this purpose. Contrary to expectations, our findings reveal that models fine-tuned on medical data or excelling in medical benchmarks do not necessarily produce high-quality messages.
%R 10.18653/v1/2025.cl4health-1.25
%U https://aclanthology.org/2025.cl4health-1.25/
%U https://doi.org/10.18653/v1/2025.cl4health-1.25
%P 298-302
Markdown (Informal)
[A Preliminary Study on NLP-Based Personalized Support for Type 1 Diabetes Management](https://aclanthology.org/2025.cl4health-1.25/) (Mitrović et al., CL4Health 2025)
ACL
- Sandra Mitrović, Federico Fontana, Andrea Zignoli, Felipe Mattioni Maturana, Christian Berchtold, Daniele Malpetti, Sam Scott, and Laura Azzimonti. 2025. A Preliminary Study on NLP-Based Personalized Support for Type 1 Diabetes Management. In Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health), pages 298–302, Albuquerque, New Mexico. Association for Computational Linguistics.