ArticlePloS one2018
Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- Three-way multi-criteria decision-making with linguistic information using prospect-regret theory and generalised TODIM for heart failure data analysis.Scientific reports · 2026Article
- Medical Artificial Intelligence: Opportunities and Challenges In Infectious Disease Management.Current medicinal chemistry · 2026Review
- 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
- Machine learning for predicting acute myocardial infarction in patients with sepsis.Scientific reports · 2024Article
- Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review.Frontiers in medicine · 2023Article
- Artificial Intelligence in Infection Management in the ICU.Critical care (London, England) · 2022Review
- Precision medicine in sepsis and septic shock: From omics to clinical tools.World journal of critical care medicine · 2022Review
- Association between neutrophil-to-albumin ratio and mortality in patients with cardiogenic shock: a retrospective cohort study.BMJ open · 2020Article
- Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.BMC medical informatics and decision making · 2020Article
- Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data.Journal of clinical medicine · 2019Article
- Application of Exercised-based Pre-rehabilitation in Perioperative Period of Patients with Gastric Cancer.Open medicine (Warsaw, Poland) · 2019Article
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Authors and funding
11 authors at 8 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Circulatory shock is a life-threatening disease that accounts for around one-third of all admissions to intensive care units (ICU). It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. In this study, the ShockOmics European project original database is used to extract attributes capable of predicting mortality due to shock in the ICU. Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. Selected features were later used to build Bayesian Networks, revealing causal relationships between features and ICU outcome. The main result is a subset of predictive features that includes well-known indicators such as the SOFA and APACHE II scores, but also less commonly considered ones related to cardiovascular function assessed through echocardiograpy or shock treatment with pressors. Importantly, certain selected features are shown to be most predictive at certain time-steps. This means that, as shock progresses, different attributes could be prioritized. Clinical traits obtained at 24h. from ICU admission are shown to accurately predict cardiogenic and septic shock mortality, suggesting that relevant life-saving decisions could be made shortly after ICU admission.
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