Sepsis Diagnosis

Sepsis diagnosis, a critical challenge due to its high mortality rate, is actively being improved through research focusing on early and accurate identification. Current efforts leverage machine learning models, including Random Forests, XGBoost, neural networks, and ensemble methods, often incorporating physiological data like heart rate variability and novel imaging biomarkers from hyperspectral imaging to enhance predictive accuracy. These advancements aim to improve patient outcomes by enabling earlier interventions and highlighting the need for addressing disparities in diagnostic performance across different patient populations. The ultimate goal is to develop robust, reliable, and equitable diagnostic tools that can be readily implemented in clinical settings.

Papers

August 19, 2024

New spectral imaging biomarkers for sepsis and mortality in intensive care
Silvia Seidlitz, Katharina Hölzl, Ayca von Garrel, Jan Sellner, Stephan Katzenschlager, Tobias Hölle, Dania Fischer, Maik von der Forst, Felix C. F. Schmitt, Markus A. Weigand, Lena Maier-Hein, Maximilian Dietrich
Hyperspectral Imaging Intensive Care Year Specific Mortality Surface Standard Sepsis Imaging Biomarkers Sepsis Diagnosis

August 1, 2024

Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability
Sai Balaji, Christopher Sun, Anaiy Somalwar
Sepsis Prediction Heart Rate Variability Sepsis Diagnosis Sepsis Detection

July 11, 2024

Advanced Meta-Ensemble Machine Learning Models for Early and Accurate Sepsis Prediction to Improve Patient Outcomes
MohammadAmin Ansari Khoushabar, Parviz Ghafariasl
Random Forest Area Under the ROC Curve Random Forest Model Sepsis Prediction Clinical Outcome Ensemble Machine Learning Sepsis Diagnosis

September 17, 2023

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Shao Zhang, Jianing Yu, Xuhai Xu, Changchang Yin, Yuxuan Lu, Bingsheng Yao, Melanie Tory, Lace M. Padilla, Jeffrey Caterino, Ping Zhang, Dakuo Wang
Case Study Human Ai Collaboration Clinical Decision Support Clinical Decision Sepsis Prediction Sepsis Treatment Sepsis Diagnosis

June 21, 2023

Investigating Poor Performance Regions of Black Boxes: LIME-based Exploration in Sepsis Detection
Mozhgan Salimiparsa, Surajsinh Parmar, San Lee, Choongmin Kim, Yonghwan Kim, Jang Yong Kim
Inherent Interpretability Black Box Local Interpretable Model Agnostic Explanation Performance Degradation Agnostic Exploration Sepsis Detection Sepsis Diagnosis

February 11, 2023

Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals
Ruiqing Ding, Fangjie Rong, Xiao Han, Leye Wang
Collaborative Learning Data Scarce Understanding Cross Sepsis Diagnosis

December 15, 2021

Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients
Hanyin Wang, Yikuan Li, Andrew Naidech, Yuan Luo
Machine Learning Health Disparity Social Determinant Mortality Prediction Sepsis Prediction Sepsis Diagnosis

Sepsis Diagnosis

Papers

New spectral imaging biomarkers for sepsis and mortality in intensive care

Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability

Advanced Meta-Ensemble Machine Learning Models for Early and Accurate Sepsis Prediction to Improve Patient Outcomes

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

Investigating Poor Performance Regions of Black Boxes: LIME-based Exploration in Sepsis Detection

Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals

Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients