@inproceedings{dasgupta-etal-2025-predicting,
title = "Predicting {ICU} Length of Stay for Patients using Latent Categorization of Health Conditions",
author = "Dasgupta, Tirthankar and
Sinha, Manjira and
Jana, Sudeshna",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.35/",
doi = "10.18653/v1/2025.naacl-industry.35",
pages = "422--430",
ISBN = "979-8-89176-194-0",
abstract = "Predicting the duration of a patient{'}s stay in an Intensive Care Unit (ICU) is a critical challenge for healthcare administrators, as it impacts resource allocation, staffing, and patient care strategies. Traditional approaches often rely on structured clinical data, but recent developments in language models offer significant potential to utilize unstructured text data such as nursing notes, discharge summaries, and clinical reports for ICU length-of-stay (LoS) predictions. In this study, we introduce a method for analyzing nursing notes to predict the remaining ICU stay duration of patients. Our approach leverages a joint model of latent note categorization, which identifies key health-related patterns and disease severity factors from unstructured text data. This latent categorization enables the model to derive high-level insights that influence patient care planning. We evaluate our model on the widely used MIMIC-III dataset, and our preliminary findings show that it significantly outperforms existing baselines, suggesting promising industrial applications for resource optimization and operational efficiency in healthcare settings."
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<abstract>Predicting the duration of a patient’s stay in an Intensive Care Unit (ICU) is a critical challenge for healthcare administrators, as it impacts resource allocation, staffing, and patient care strategies. Traditional approaches often rely on structured clinical data, but recent developments in language models offer significant potential to utilize unstructured text data such as nursing notes, discharge summaries, and clinical reports for ICU length-of-stay (LoS) predictions. In this study, we introduce a method for analyzing nursing notes to predict the remaining ICU stay duration of patients. Our approach leverages a joint model of latent note categorization, which identifies key health-related patterns and disease severity factors from unstructured text data. This latent categorization enables the model to derive high-level insights that influence patient care planning. We evaluate our model on the widely used MIMIC-III dataset, and our preliminary findings show that it significantly outperforms existing baselines, suggesting promising industrial applications for resource optimization and operational efficiency in healthcare settings.</abstract>
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%0 Conference Proceedings
%T Predicting ICU Length of Stay for Patients using Latent Categorization of Health Conditions
%A Dasgupta, Tirthankar
%A Sinha, Manjira
%A Jana, Sudeshna
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F dasgupta-etal-2025-predicting
%X Predicting the duration of a patient’s stay in an Intensive Care Unit (ICU) is a critical challenge for healthcare administrators, as it impacts resource allocation, staffing, and patient care strategies. Traditional approaches often rely on structured clinical data, but recent developments in language models offer significant potential to utilize unstructured text data such as nursing notes, discharge summaries, and clinical reports for ICU length-of-stay (LoS) predictions. In this study, we introduce a method for analyzing nursing notes to predict the remaining ICU stay duration of patients. Our approach leverages a joint model of latent note categorization, which identifies key health-related patterns and disease severity factors from unstructured text data. This latent categorization enables the model to derive high-level insights that influence patient care planning. We evaluate our model on the widely used MIMIC-III dataset, and our preliminary findings show that it significantly outperforms existing baselines, suggesting promising industrial applications for resource optimization and operational efficiency in healthcare settings.
%R 10.18653/v1/2025.naacl-industry.35
%U https://aclanthology.org/2025.naacl-industry.35/
%U https://doi.org/10.18653/v1/2025.naacl-industry.35
%P 422-430
Markdown (Informal)
[Predicting ICU Length of Stay for Patients using Latent Categorization of Health Conditions](https://aclanthology.org/2025.naacl-industry.35/) (Dasgupta et al., NAACL 2025)
ACL