---
title: "Machine learning predicts in-hospital mortality for ICU patients with diabetes and sepsis"
id: "pubmed-42907983"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42907983"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42907983/"
doi: "10.1186/s12911-026-03580-4"
published_at: "2026-10-22T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Machine learning predicts in-hospital mortality for ICU patients with diabetes and sepsis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42907983
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42907983/)
- **DOI:** [10.1186/s12911-026-03580-4](https://doi.org/10.1186%2Fs12911-026-03580-4)
- **Published At:** 2026-10-22T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Investigators performed a retrospective cohort study using the **MIMIC-IV** database to model in-hospital mortality among adult ICU patients with concurrent **sepsis** and **diabetes mellitus**. - The study cohort included 7,716 patients; 1,357 (17.6%) experienced in-hospital death, as reported by the authors. - Six algorithms were trained and evaluated: Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), XGBoost and LightGBM. - Model discrimination was assessed using area under the ROC curve (AUC); GBDT achieved the highest AUC of 0.792 (95% CI: 0.769–0.814). - The conventional **SOFA** score was used as a comparator; machine learning models outperformed it, per the reported AUCs. - The authors applied SHAP values to the best-performing model to rank predictor importance and improve interpretability. - The analysis relied on secondary, de-identified data from a single publicly available ICU database; institutional review board approval and individual consent were waived. - Investigators noted the need for multi-center validation and prospective trials to test generalizability and clinical impact of these predictive models.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Department of Anesthesiology, Shanghai Jing'an District Zhabei Central Hospital, Shanghai, People's Republic of China. * 2 Department of Anesthesiology, Shanghai Jing'an District Zhabei Central Hospital, Shanghai, People's Republic of China. wangyu_12123@163.com. # Contributed equally. * PMID: **42907983** * DOI: [ 10.1186/s12911-026-03580-4 ](https://doi.org/10.1186/s12911-026-03580-4) Item in Clipboard # Application of machine learning prediction models for in-hospital mortality in diabetic sepsis patients: a study based on the MIMIC IV database Zongxin Shen et al. BMC Med Inform Decis Mak. 2026. Show details Display options Display options Format Abstract PubMed PMID BMC Med Inform Decis Mak Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22BMC+Med+Inform+Decis+Mak%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22BMC+Med+Inform+Decis+Mak%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) . 2026 Oct 22;26(1):426. doi: 10.1186/s12911-026-03580-4. ### Authors [Zongxin Shen](https://pubmed.ncbi.nlm.nih.gov/?term=Shen+Z&cauthor_id=42907983)[#](https://pubmed.ncbi.nlm.nih.gov/42907983/#short-view-equal-contrib-explanation "Contributed equally")[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907983/#short-view-affiliation-1 "Department of Anesthesiology, Shanghai Jing'an District Zhabei Central Hospital, Shanghai, People's Republic of China."), [Haijiao Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+H&cauthor_id=42907983)[#](https://pubmed.ncbi.nlm.nih.gov/42907983/#short-view-equal-contrib-explanation "Contributed equally")[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907983/#short-view-affiliation-1 "Department of Anesthesiology, Shanghai Jing'an District Zhabei Central Hospital, Shanghai, People's Republic of China."), [Yu Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+Y&cauthor_id=42907983)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42907983/#short-view-affiliation-2 "Department of Anesthesiology, Shanghai Jing'an District Zhabei Central Hospital, Shanghai, People's Republic of China. wangyu_12123@163.com.") ### Affiliations * 1 Department of Anesthesiology, Shanghai Jing'an District Zhabei Central Hospital, Shanghai, People's Republic of China. * 2 Department of Anesthesiology, Shanghai Jing'an District Zhabei Central Hospital, Shanghai, People's Republic of China. wangyu_12123@163.com. # Contributed equally. * PMID: **42907983** * DOI: [ 10.1186/s12911-026-03580-4 ](https://doi.org/10.1186/s12911-026-03580-4) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## Abstract **Background:** Sepsis complicated by diabetes mellitus represents a significant clinical challenge in intensive care units (ICUs), associated with substantial morbidity and mortality. Accurate prognostication of in-hospital mortality in this complex patient population is crucial for optimizing clinical decision making, resource allocation, and patient management strategies. **Methods:** We conducted a retrospective cohort study utilizing data from the Medical Information Mart for Intensive Care (MIMIC-IV) database. The study focused on adult ICU patients with concurrent diagnoses of sepsis and diabetes mellitus. Six machine learning algorithms were evaluated for their predictive performance: Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), XGBoost and LightGBM. Additionally, we assessed the prognostic capability of the Sequential Organ Failure Assessment (SOFA) score. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we employed SHAP (SHapley Additive exPlanations) values to elucidate the relative importance of predictor variables. **Results:** The study cohort comprised 7716 patients, with 1357 (17.6%) experiencing in-hospital mortality. Among the machine learning models evaluated, GBDT exhibited superior predictive performance achieving an AUC of 0.792 (95% CI: 0.769-0.814). This performance surpassed that of other machine learning algorithms and the conventional SOFA score. SHAP analysis provided valuable insights into the hierarchical importance of various clinical and demographic features contributing to the GBDT model's predictions. **Conclusion:** Our findings demonstrate the potential of machine learning approaches in accurately predicting in-hospital mortality among ICU patients with sepsis and diabetes mellitus. These advanced analytical techniques outperform traditional scoring systems such as SOFA. The integration of SHAP values enhances model interpretability, potentially facilitating the adoption of these predictive tools in clinical practice. Further multi-center validation studies and prospective trials are warranted to confirm the generalizability of these results and to explore the clinical impact of implementing such models in diverse healthcare settings. **Keywords:** Diabetes mellitus; Intensive care unit; Machine learning; Mortality prediction; Sepsis. © 2026. The Author(s). [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declarations. Ethics approval and consent to participate: This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. The research involved secondary analysis of the publicly accessible and de-identified MIMIC-IV database. Therefore, separate institutional review board approval and individual informed consent were waived. Consent for publication: Not applicable. This manuscript does not contain any individual person’s identifiable data. Competing interests: The authors declare no competing interests. ## References 1. 1. Schuetz P, Castro P, Shapiro NI. Diabetes and sepsis: preclinical findings and clinical relevance. Diabetes Care. 2011;34:771–8. - [DOI](https://doi.org/10.2337/dc10-1185) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/21357364/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/3041224/) 2. 1. Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395:200–11. - [DOI](https://doi.org/10.1016/s0140-6736\(19\)32989-7) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/31954465/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/6970225/) 3. 1. Esper AM, Moss M, Martin GS. The effect of diabetes mellitus on organ dysfunction with sepsis: an epidemiological study. Crit Care. 2009;13:R18. - [DOI](https://doi.org/10.1186/cc7717) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/19216780/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/2688136/) 4. 1. van Vught LA, Holman R, de Jonge E, et al. Diabetes is not associated with increased 90-day mortality risk in critically ill patients with sepsis. Crit Care Med. 2017;45:e1026–35. - [DOI](https://doi.org/10.1097/ccm.0000000000002590) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/28737575/) 5. 1. Frydrych LM, Bian G, O’Lone DE, et al. Obesity and type 2 diabetes mellitus drive immune dysfunction, infection development, and sepsis mortality. J Leukoc Biol. 2018;104:525–34. - [DOI](https://doi.org/10.1002/jlb.5vmr0118-021rr) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/30066958/) Show all 31 references ## MeSH terms * Aged Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Aged%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Aged) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Boosting Machine Learning Algorithms Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Boosting+Machine+Learning+Algorithms%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Boosting+Machine+Learning+Algorithms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Classification Algorithms Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Classification+Algorithms%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Classification+Algorithms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Databases, Factual Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Databases%2C+Factual%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Databases%2C+Factual) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Diabetes Complications* / mortality Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Diabetes+Complications%2Fmortality%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Diabetes+Complications) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Diabetes Mellitus* / mortality Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Diabetes+Mellitus%2Fmortality%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Diabetes+Mellitus) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Female Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Female%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Female) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Hospital Mortality* Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Hospital+Mortality%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Hospital+Mortality) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Humans Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Humans%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Humans) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Intensive Care Units / statistics & numerical data Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Intensive+Care+Units%2Fstatistics+and+numerical+data%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Intensive+Care+Units) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Machine Learning* Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Machine+Learning%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Machine+Learning) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Male Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Male%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Male) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Middle Aged Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Middle+Aged%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Middle+Aged) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Prediction Algorithms Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Prediction+Algorithms%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Prediction+Algorithms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Predictive Learning Models Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Predictive+Learning+Models%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Predictive+Learning+Models) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907983/) * Random Forest Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Random+Forest%22%5BMeSH%5D&so
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