ArticleBMJ open2019
Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Implementation and feasibility evaluation of a standard set of intensive care unit discharge criteria in a prospective before-after observational pilot study.BMC health services research · 2026Observational
- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
- Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.BMJ health & care informatics · 2026Article
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Article
- Advances in Machine Learning Models for Healthcare Applications: A Precise and Patient-Centric Approach.Current pharmaceutical design · 2025Review
- Transition experiences of patients and families upon discharge from intensive care: a scoping review.Revista latino-americana de enfermagem · 2025Article
- Explainable hierarchical clustering for patient subtyping and risk prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier.Healthcare (Basel, Switzerland) · 2023Article
- Strategies of Predictive Schemes and Clinical Diagnosis for Prognosis Using MIMIC-III: A Systematic Review.Healthcare (Basel, Switzerland) · 2023Review
- Tree-based survival analysis improves mortality prediction in cardiac surgery.Frontiers in cardiovascular medicine · 2023Article
- Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit.Communications medicine · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Delphi study to derive expert consensus on a set of criteria to evaluate discharge readiness for adult ICU patients to be discharged to a general ward-European perspective.BMC health services research · 2022Article
- A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units.Healthcare (Basel, Switzerland) · 2022Article
- State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.JMIR medical informatics · 2022Review
- Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines.Frontiers in medicine · 2022Article
- Optimization of critical care pharmacy clinical services: A gap analysis approach.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2021Article
- Production of Aluminum AA6061 Hybrid Nanocomposite from Waste Metal Using Hot Extrusion Process: Strength Performance and Prediction by RSM and Random Forest.Materials (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThe primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.
designWe used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.
settingBristol Royal Infirmary general intensive care unit (GICU). PATIENTS: Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.
resultsIn both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.
conclusionsOur findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.
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