ArticleScientific reports2022
Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- Article
- Review
- Early postoperative factors associated with in-hospital mortality after emergency noncardiac surgery: A Japanese nationwide registry-based intensive care unit cohort study.Scientific reports · 2026Observational
- Perioperative nonsteroidal anti-inflammatory drugs and the risk for anastomotic leakage in patients undergoing major gastrointestinal surgery: a retrospective cohort study.BMC anesthesiology · 2025Article
- FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The impact of feature combinations on machine learning models for in-hospital mortality prediction.Scientific reports · 2025Article
- Analyzing Information Disparities across Modalities in Mortality Prediction.medRxiv : the preprint server for health sciences · 2025Article
- AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.Science advances · 2025Observational
- Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries.Intensive care medicine experimental · 2025Article
- CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.BMC medical informatics and decision making · 2025Article
- Correspondence on "Implementing palliative care in the intensive care unit: a systematic review and mapping of knowledge to the implementation research logic model".Intensive care medicine · 2025Article
- Fast and interpretable mortality risk scores for critical care patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Comparing Single-Hospital and National Models to Predict 30-Day Inpatient Mortality.Journal of general internal medicine · 2025Article
- Machine Learning Models for the Early Real-Time Prediction of Deterioration in Intensive Care Units-A Novel Approach to the Early Identification of High-Risk Patients.Journal of clinical medicine · 2025Article
- Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study.BMC emergency medicine · 2025Observational
- Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.PloS one · 2025Article
- Transforming critical care: the digital revolution's impact on intensive care units.Frontiers in digital health · 2025Review
- Validation of the inadequate delivery of oxygen index in an adult cardiovascular intensive care unit.JTCVS open · 2024Article
- Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources.Proceedings of machine learning research · 2024Article
- Repositioning of ezetimibe for the treatment of idiopathic pulmonary fibrosis.The European respiratory journal · 2024Article
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7 authors.
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Abstract
Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy that periodically includes the latest clinical data and can be updated to reflect local characteristics. We extracted data from all adult patients admitted to the ICUs of two university hospitals with different characteristics from 2006 to 2020, and a total of 85,146 patients were included in this study. Machine learning algorithms were trained to predict in-hospital mortality. The predictive performance of conventional scoring models and machine learning algorithms was assessed by the area under the receiver operating characteristic curve (AUROC). The conventional scoring models had various predictive powers, with the SAPS III (AUROC 0.773 [0.766-0.779] for hospital S) and APACHE III (AUROC 0.803 [0.795-0.810] for hospital G) showing the highest AUROC among them. The best performing machine learning models achieved an AUROC of 0.977 (0.973-0.980) in hospital S and 0.955 (0.950-0.961) in hospital G. The use of ML models in conjunction with conventional scoring systems can provide more useful information for predicting the prognosis of critically ill patients. In this study, we suggest that the predictive model can be made more robust by training with the individual data of each hospital.
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