ArticleBMC medical research methodology2022
Comparative analysis of explainable machine learning prediction models for hospital mortality.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- Explainable machine learning model reveals its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation.BMC cardiovascular disorders · 2023Trial
- Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.BMC medical informatics and decision making · 2026Article
- SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.BMC medical informatics and decision making · 2026Article
- Article
- UbiQTree: Uncertainty quantification in XAI with tree ensembles.Patterns (New York, N.Y.) · 2026Article
- [Unmet need for contraception in the racialized population in Colombia: analysis of the results of the 2015 National Demographic and Health Survey].Cadernos de saude publica · 2026Article
- Explainable artificial intelligence in anesthesiology prediction models: bridging the gap from black-box algorithms to clinical decision support.Frontiers in medicine · 2026Review
- Predicting Mortality in Intensive Care Unit Patients With Allergic Bronchopulmonary Aspergillosis (ABPA) Using an Interpretable Machine Learning Model: A Retrospective Cohort Study.Canadian respiratory journal · 2026Article
- Identification and risk-factor analysis for individuals at high risk for keratoconus via machine learning and logistic regression.PloS one · 2026Article
- An explainable ensemble machine learning model using baseline blood transcriptomics to predict Parkinson's disease motor progression.Frontiers in digital health · 2026Article
- Development and external validation of an interpretable machine learning model for diagnosing coronary heart disease in patients with type 2 diabetes and MASLD.Frontiers in endocrinology · 2026Article
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.BMC infectious diseases · 2025Article
- A Hybrid MCDM and Machine Learning Framework for Thalassemia Risk Assessment in Pregnant Women.Diagnostics (Basel, Switzerland) · 2025Article
- The impact of feature combinations on machine learning models for in-hospital mortality prediction.Scientific reports · 2025Article
- Prediction of prolonged length of stay from first 2 days of hospitalization data: a SHAP value-based variable selection method for a simplified model.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2025Article
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- [Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Predicting cardiac resynchronization therapy response: development and validation of a single photon emission computed tomography-based nomogram.Quantitative imaging in medicine and surgery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acceptance of this technology in healthcare. This mistrust is often credited to the shortage of model explainability and interpretability, where the relationship between the input and output of the models is unclear. Improving trust requires the development of more transparent ML methods.
methodsIn this paper, we use the publicly available eICU database to construct a number of ML models before examining their internal behaviour with SHapley Additive exPlanations (SHAP) values. Our four models predicted hospital mortality in ICU patients using a selection of the same features used to calculate the APACHE IV score and were based on random forest, logistic regression, naive Bayes, and adaptive boosting algorithms.
resultsThe results showed the models had similar discriminative abilities and mostly agreed on feature importance while calibration and impact of individual features differed considerably and did in multiple cases not correspond to common medical theory.
conclusionsWe already know that ML models treat data differently depending on the underlying algorithm. Our comparative analysis visualises implications of these differences and their importance in a healthcare setting. SHAP value analysis is a promising method for incorporating explainability in model development and usage and might yield better and more trustworthy ML models in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.