ArticleInternational journal of molecular sciences2022
Outcome Prediction in Critically-Ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms: External Validation and Comparison with Scoring Systems.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- Integrating artificial intelligence into venous thromboembolism care: Predictive models, implementation challenges, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Integrated multi-omics and machine learning identify an interaction between SLC39A11 and phosphoinositide metabolism in deep vein thrombosis.BMC medical informatics and decision making · 2026Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.BMC medical informatics and decision making · 2025Article
- Advancing Mortality Prediction in Pulmonary Embolism Using Machine Learning Algorithms-Systematic Review and Meta-Analysis.Pulmonary circulation · 2025Review
- Cohort study of prediction of venous thromboembolism in emergency department patients with extremity symptoms.Internal and emergency medicine · 2025Article
- Genetic factors, risk prediction and AI application of thrombotic diseases.Experimental hematology & oncology · 2024Review
- A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.Translational psychiatry · 2024Article
- Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures.Scientific reports · 2024Article
- Review
- Automated machine learning for genome wide association studies.Bioinformatics (Oxford, England) · 2023Article
- Article
- Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction Models.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Intensive care unit (ICU) patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist but lack in specificity. This study focuses on two target groups, namely patients with thrombosis or cancer. The main goal is to develop and validate interpretable machine learning (ML) models to predict early and late mortality, while exploiting all available data stored in the medical record. To this end, retrospective data from two freely accessible databases, MIMIC-III and eICU, were used. Well-established ML algorithms were implemented utilizing automated and purposely built ML frameworks for addressing class imbalance. Prediction of early mortality showed excellent performance in both disease categories, in terms of the area under the receiver operating characteristic curve (
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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.