ArticleThe Journal of international medical research2023
Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.
Article in The Journal of international medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Article
- Intensive care unit outcomes and prognostic factors of colorectal cancer.World journal of gastrointestinal oncology · 2025Article
- Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.BMC medical informatics and decision making · 2025Article
- Predicting 30-day mortality in severely injured elderly patients with trauma in Korea using machine learning algorithms: a retrospective study.Journal of trauma and injury · 2024Article
- Novel decision tree models predict the overall survival of patients with submandibular gland cancer.Clinical oral investigations · 2024Article
- Interpretable early mortality prediction in oncology ICU patients: A dual-cohort validation of a LASSO-XGBoost-SHAP framework.Digital healthArticle
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches.
methodsData were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality.
resultsOverall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts.
conclusionsWe showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.
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