Papers with MIMIC-III dataset
Predicting ICU Length of Stay for Patients using Latent Categorization of Health Conditions (2025.naacl-industry)
Copied to clipboard
| 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. |
Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to assigning ICD codes to clinical text are time-consuming, labor intensive, and error-prone. |
| Approach: | They propose to adapt a Transformer-based model to a longformer model and use it to encode clinical narratives. |
| Outcome: | The proposed approach outperforms current state-of-the-art models in ICD coding with the label embeddings contributing to the good performance. |
Automatic sentence segmentation of clinical record narratives in real-world data (2024.emnlp-main)
Copied to clipboard
| Challenge: | Sentence segmentation is a linguistic task used as a pre-processing step in many NLP applications. |
| Approach: | They propose a sequence labeling classifier that predicts sentence spans using a dynamic sliding window based on the prediction of each input sequence. |
| Outcome: | The proposed method outperforms state-of-the-art systems on clinical notes and on five other datasets to assess its generalizability and performance. |