# NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients

arXiv: https://arxiv.org/abs/2304.12737 · HTML: https://mlsanity.com/paper/2304.12737/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 61 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Certifying Some Distributional Robustness with Principled Adversarial Training](https://mlsanity.com/paper/1710.10571/) (2017)
- [Self-supervised Learning is More Robust to Dataset Imbalance](https://mlsanity.com/paper/2110.05025/) (2021)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier](https://mlsanity.com/paper/1706.04152/) (2017)
- [Explainable artificial intelligence model to predict acute critical illness from electronic health records](https://mlsanity.com/paper/1912.01266/) (2019)
- [An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection](https://mlsanity.com/paper/1708.05894/) (2017)
- [Early detection of sepsis utilizing deep learning on electronic health record event sequences](https://mlsanity.com/paper/1906.02956/) (2019)
- [Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping](https://mlsanity.com/paper/1902.01659/) (2019)
- [Multi-Subset Approach to Early Sepsis Prediction](https://mlsanity.com/paper/2304.06384/) (2023)
- [All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification](https://mlsanity.com/paper/1711.04450/) (2017)
- [The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards](https://mlsanity.com/paper/2101.10790/) (2021)

## Built on by (most cited first, 1 of 1)

- [Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients](https://mlsanity.com/paper/2602.02930/) (2026)

## Abstract (arXiv)

Sepsis is a syndrome that develops in the body in response to the presence of an infection. Characterized by severe organ dysfunction, sepsis is one of the leading causes of mortality in Intensive Care Units (ICUs) worldwide. These complications can be reduced through early application of antibiotics. Hence, the ability to anticipate the onset of sepsis early is crucial to the survival and well-being of patients. Current machine learning algorithms deployed inside medical infrastructures have demonstrated poor performance and are insufficient for anticipating sepsis onset early. Recently, deep learning methodologies have been proposed to predict sepsis, but some fail to capture the time of onset (e.g., classifying patients' entire visits as developing sepsis or not) and others are unrealistic for deployment in clinical settings (e.g., creating training instances using a fixed time to onset, where the time of onset needs to be known apriori). In this paper, we first propose a novel but realistic prediction framework that predicts each morning whether sepsis onset will occur within the next 24 hours using the most recent data collected the previous night, when patient-provider ratios are higher due to cross-coverage resulting in limited observation to each patient. However, as we increase the prediction rate into daily, the number of negative instances will increase, while that of positive instances remain the same. This causes a severe class imbalance problem making it hard to capture these rare sepsis cases. To address this, we propose a nightly profile representation learning (NPRL) approach. We prove that NPRL can theoretically alleviate the rare event problem and our empirical study using data from a level-1 trauma center demonstrates the effectiveness of our proposal.