Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model

Aug 6, 2026

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The study constructs and applies a clinical visualization system for predicting 30-day mortality in sepsis patients using an improved machine learning approach. A retrospective cohort of 1,050 patients admitted from January 2010 to August 2023 is analyzed, split into survival and death groups by 30-day outcomes. A self-weighted self-evolutionary learning model (SWSELM) is developed to determine independent mortality risk factors and support risk visualization for clinical use. Performance is evaluated with ROC-AUC and PR-AUC, and key predictors are identified.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Objective

Methods

Study design and data collection

Model development and visualization system

Results

Conclusion

TYPE Original Research PUBLISHED 21 May 2025 DOI 10.3389/fphys.2025.1560659 OPEN ACCESS EDITED BY Qinghe Meng, Upstate Medical University, United States REVIEWED BY Hong Cheng, Capital Medical University, China Markus Graf, Heilbronn University, Germany Ahmet Çifci, Mehmet Akif Ersoy University, Türkiye *CORRESPONDENCE Fei Tong, [tongfei9246@163.com](tongfei9246@163.com) RECEIVED 21 January 2025 ACCEPTED 09 May 2025 PUBLISHED 21 May 2025 CITATION Chen T, Zhang X, Yu Q, Yang Q, Yuan L and Tong F (2025) Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model. Front. Physiol. 16:1560659. doi: 10.3389/fphys.2025.1560659 COPYRIGHT © 2025 Chen, Zhang, Yu, Yang, Yuan and Tong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model Ting Chen 1, Xuefeng Zhang 1, Qunfeng Yu 2, Qin Yang 2, Lingmin Yuan 2 and Fei Tong 3* 1 Emergency Department of Longyou County People’s Hospital, Quzhou, Zhejiang, China, 2 Intensive Care Unit of Longyou County People’s Hospital, Quzhou, Zhejiang, China, 3 Department of Thoracic Surgery, Longyou County People's Hospital, Quzhou, China Objective: To explore the construction and clinical visualization application of a mortality risk prediction model for sepsis patients based on an improved machine learning model. Methods: This retrospective study analyzed 1,050 sepsis patients admitted to Longyou County People’s Hospital between January 2010 and August 2023 . Patients were divided into a survival group (n = 877) and a death group (n = 173) based on their 30-day mortality status. Clinical and laboratory data were collected and used as feature variables. A Self-Weighted Self-Evolutionary Learning Model (SWSELM) was developed to identify independent risk factors for sepsis mortality and to create a visualization system for clinical application. Results: The improved algorithm significantly outperformed other algorithms on 23 standard test functions. The SWSELM model achieved ROC-AUCand PR-AUC values of 0.9760 and 0.9624, respectively, on the training set, and 0.9387 and 0.9390, respectively, on the test set, both significantly higher than those of three other prediction models. The SWSELM model identified 10 important features, with multivariate logistic regression retaining five variables: B-type Natriuretic Peptide Precursor (NT-proBNP), Lactate, Albumin, Oxygenation Index, and Mean Arterial Pressure (MAP) (OR = 4.889, 3.770, 3.083, 1.872, 1.297), consistent with the top five features selected by the SWSELM model. Conclusion: NT-proBNP, Lactate, Albumin, Oxygenation Index, and Mean Arterial Pressure are independent risk factors for mortality in sepsis patients. This study successfully created a self-evolutionary prediction model using machine learning methods, demonstrating significant clinical application potential and value for broader implementation. KEYWORDS machine learning, sepsis, mortality, NT-ProBNP, prediction, visualization Frontiers in Physiology 01 [frontiersin.org](frontiersin.org) 1 Introduction Sepsis, defined as a life-threatening systemic infection, arises from a dysregulated host response to infection that can lead to organ dysfunction. However, the progression to life-threatening complications hinges on early recognition and timely interventions (Liu et al., 2022) . In critical care

FAQs

What is the objective of this study?

To construct and provide a clinical visualization application for a mortality risk prediction model for sepsis patients using an improved machine learning model.

How was the predictive model developed and evaluated?

A retrospective dataset of 1,050 sepsis patients was used, and the SWSELM model was trained and tested with ROC-AUC and PR-AUC metrics on both training and test sets.

Which factors were identified as independent risk predictors for mortality?

NT-proBNP, Lactate, Albumin, Oxygenation Index, and Mean Arterial Pressure (MAP) were retained as independent risk factors in multivariate logistic regression.

Aug 6, 2026

Upload by Eliana