ArticleBMC medical informatics and decision making2022
Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.BMC medical informatics and decision making · 2025Pooled it
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- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- First-Day Glycemic Exposure and 28-Day Mortality in the ICU: A Multicohort Study.Research square · 2026Article
- First-Day Glycemic Exposure and 28-Day Mortality in the ICU: A Multicenter Cohort Study.Research square · 2026Article
- Data augmentation alters feature importance in XGBoost for CVD prediction.Scientific reports · 2025Article
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Artificial Intelligence in the Management of Patients with Respiratory Failure Requiring Mechanical Ventilation: A Scoping Review.Journal of clinical medicine · 2024Article
- Mortality rate analysis of patients on invasive mechanical ventilation in the intensive care unit on day 28.Biomedical reports · 2024Article
- Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR).Polymers · 2024Article
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- Toward explainable deep learning in healthcare through transition matrix and user-friendly features.Frontiers in artificial intelligence · 2024Article
- Explainable Machine Learning to Predict Successful Weaning of Mechanical Ventilation in Critically Ill Patients Requiring Hemodialysis.Healthcare (Basel, Switzerland) · 2023Article
- Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.Health economics review · 2023Article
- Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients.Frontiers in nutrition · 2023Article
- Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan.BMC anesthesiology · 2022Article
- Week-One Anaemia was Associated with Increased One-Year Mortality in Critically Ill Surgical Patients.International journal of clinical practice · 2022Article
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients.
methodsWe retrospectively included patients who were admitted to intensive care units during 2015-2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot (PDP), and local interpretable model-agnostic explanations (LIME) to explain the established model.
resultsWe enrolled 6994 patients and found the accuracy was similar among the three ML models, and the area under the curve value of using XGBoost to predict 30-day, 90-day and 1-year mortality were 0.858, 0.839 and 0.816, respectively. The calibration curve and decision curve analysis further demonstrated accuracy and applicability of models. SHAP summary plot and PDP plot illustrated the discriminative point of APACHE (acute physiology and chronic health exam) II score, haemoglobin and albumin to predict 1-year mortality. The application of LIME and SHAP force plots quantified the probability of 1-year mortality and algorithm of key features at individual patient level.
conclusionsWe used an explainable ML approach, mainly XGBoost, SHAP and LIME plots to establish an explainable 1-year mortality prediction ML model in critically ill ventilated patients.
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