Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling
Abstract Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing therapeutic interventions and improving...
Saved in:
| Main Authors: | Kaida Cai, Xiaofang Yang, Zhengyan Wang, Wenzhi Fu, Hanwen Liu, Fatemeh Mahmoudi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05876-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Novel Aggregated Multiple Imputation Approach for Enhanced Survival Prediction and Classification on Breast Cancer and Lung Cancer Data
by: P. Deepa, et al.
Published: (2024-01-01) -
Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates
by: Yuming Shi, et al.
Published: (2025-01-01) -
SSL-SurvFormer: A Self-Supervised Learning and Continuously Monotonic Transformer Network for Missing Values in Survival Analysis
by: Quang-Hung Le, et al.
Published: (2025-03-01) -
SMART: Structured Missingness Analysis and Reconstruction Technique for credit scoring
by: Seongil Han, et al.
Published: (2025-04-01) -
Machine learning approaches for imputing missing meteorological data in Senegal
by: Mory Toure, et al.
Published: (2025-09-01)