# Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes

arXiv: https://arxiv.org/abs/1811.02757 · HTML: https://mlsanity.com/paper/1811.02757/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 11 of 11)

- [Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences](https://mlsanity.com/paper/2201.11838/) (2022)
- [A Comparative Study of Pretrained Language Models for Long Clinical Text](https://mlsanity.com/paper/2301.11847/) (2023)
- [Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation](https://mlsanity.com/paper/2104.03969/) (2021)
- [Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes](https://mlsanity.com/paper/2112.01230/) (2021)
- [AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury](https://mlsanity.com/paper/2205.03695/) (2022)
- [AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease](https://mlsanity.com/paper/2212.06042/) (2022)
- [Multi-objective Representation for Numbers in Clinical Narratives: A CamemBERT-Bio-Based Alternative to Large-Scale LLMs](https://mlsanity.com/paper/2405.18448/) (2024)
- [Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population](https://mlsanity.com/paper/2111.09507/) (2021)
- [Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients](https://mlsanity.com/paper/2112.08224/) (2021)
- [Using deep convolutional neural networks to classify poisonous and edible mushrooms found in China](https://mlsanity.com/paper/2210.10351/) (2022)
- [Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units](https://mlsanity.com/paper/2401.04368/) (2024)

## Abstract (arXiv)

Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate the risk of new AKI onset. We generated models from clinical notes within the first 24 hours following intensive care unit (ICU) admission extracted from Medical Information Mart for Intensive Care III (MIMIC-III). From the clinical notes, we generated clinically meaningful word and concept representations and embeddings, respectively. Five supervised learning classifiers and knowledge-guided deep learning architecture were used to construct prediction models. The best configuration yielded a competitive AUC of 0.779. Our work suggests that natural language processing of clinical notes can be applied to assist clinicians in identifying the risk of incident AKI onset in critically ill patients upon admission to the ICU.