# Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality

arXiv: https://arxiv.org/abs/2408.01612 · HTML: https://mlsanity.com/paper/2408.01612/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Random forests for high-dimensional longitudinal data](https://mlsanity.com/paper/1901.11279/) (2019)
- [Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data](https://mlsanity.com/paper/2407.14211/) (2024)
- [Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques](https://mlsanity.com/paper/2407.15302/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Novel Multi-Task Teacher-Student Architecture with Self-Supervised Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis Mortality Prediction](https://mlsanity.com/paper/2502.16834/) (2025)
- [ParsiPy: NLP Toolkit for Historical Persian Texts in Python](https://mlsanity.com/paper/2503.17810/) (2025)

## Abstract (arXiv)

Sepsis is a severe condition responsible for many deaths in the United States and worldwide, making accurate prediction of outcomes crucial for timely and effective treatment. Previous studies employing machine learning faced limitations in feature selection and model interpretability, reducing their clinical applicability. This research aimed to develop an interpretable and accurate machine learning model to predict in-hospital sepsis mortality, addressing these gaps. Using ICU patient records from the MIMIC-III database, we extracted relevant data through a combination of literature review, clinical input refinement, and Random Forest-based feature selection, identifying the top 35 features. Data preprocessing included cleaning, imputation, standardization, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, resulting in a dataset of 4,683 patients with 17,429 admissions. Five models-Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor-were developed and evaluated. The Random Forest model demonstrated the best performance, achieving an accuracy of 0.90, AUROC of 0.97, precision of 0.93, recall of 0.91, and F1-score of 0.92. These findings underscore the potential of data-driven machine learning approaches to improve critical care, offering clinicians a powerful tool for predicting in-hospital sepsis mortality and enhancing patient outcomes.