@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://preview.aclanthology.org/landing_page/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."
}