Predicting ICU Length of Stay for Patients using Latent Categorization of Health Conditions (2025.naacl-industry)
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| Challenge: | Traditional approaches to predicting the duration of a patient's stay in an Intensive Care Unit (ICU) rely on structured clinical data, but recent advances in language models offer significant potential to utilize unstructured text data for ICU length-of-stay (LoS) predictions. |
| Approach: | They propose a method for analyzing nursing notes to predict ICU length-of-stay of patients. |
| Outcome: | The proposed model outperforms baseline models on the MIMIC-III dataset and shows that it significantly outperformed existing models. |
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| Challenge: | Existing work on monitoring patients in ICU has focused on using time series signals from medical instruments. |
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Predicting in-hospital mortality by combining clinical notes with time-series data (2021.findings-acl)
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| Challenge: | In intensive care units, patient health is monitored through vital signals and clinical notes . previous work focused on predicting patient health using time-series data gathered from medical devices . |
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| Challenge: | Clinical notes have a long time span over multiple long documents. |
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A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients (2020.lrec-1)
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Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi
| Challenge: | Currently, most medical data is generated and stored in unstructured, text-based format. |
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| Challenge: | Existing studies on the ICD coding task focus on extracting codes from the discharge summary, but there is potential to automate the task by identifying relevant information from clinical notes. |
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Data Drift in Clinical Outcome Prediction from Admission Notes (2024.lrec-main)
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Paul Grundmann, Jens-Michalis Papaioannou, Tom Oberhauser, Thomas Steffek, Amy Siu, Wolfgang Nejdl, Alexander Loeser
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Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration (2021.eacl-main)
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Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, Alexander Loeser
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Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities (2025.acl-long)
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Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness (2026.findings-acl)
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| Challenge: | Existing methods to accommodate missingness in clinical time series, but how to extract and use information carried by the observation process itself remains underexplored. |
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Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)
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| Challenge: | Recent work on automated ICD coding learn mappings between low-dimensional representations of clinical text reports and codes. |
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