Papers by Sudeshna Jana
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. |
DisGraph-RP: Graph-Augmented Temporal Modeling with Aspect-Based Contrastive Encoding of Discharge Summary for Readmission Prediction (2026.eacl-industry)
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| Challenge: | Recent data indicate that nearly 15% of hospitalized patients in the U.S. are readmitted soon after discharge. |
| Approach: | They propose a graph-augmented temporal modeling framework that integrates structured discourse-aware text representation with cross-admission relational reasoning. |
| Outcome: | The proposed model improves on baseline models and prompting-based models on real-world datasets. |