ArticleFrontiers in medicine2020
A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.
Article in Frontiers in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 61 citations in OpenAlex.
- Development and external validation of the HCH and HPMS prognostic indices for sepsis: a retrospective model development study using a Multi-Objective Non-Newtonian Fluid optimization algorithm.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.BMC medical informatics and decision making · 2026Article
- Cross-algorithm machine learning and consensus features for early disseminated intravascular coagulation risk prediction in sepsis-induced coagulopathy.Journal of translational medicine · 2026Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Neutrophil-to-lymphocyte ratio predicts mortality for sepsis-induced coagulopathy: a retrospective study.European journal of medical research · 2025Article
- Development and validation of machine learning models to predict in-hospital mortality in ICU patients with sepsis and chronic kidney disease.BMC infectious diseases · 2025Article
- Development and validation of a machine-learning model for predicting prognosis in critically ill patients undergoing major surgery.BMC medical informatics and decision making · 2025Article
- [Research advances on the application of artificial intelligence technology in the diagnosis and treatment of sepsis patients].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2025Review
- Advanced machine learning models for prediction of readmission and mortality risks in patients with chronic obstructive pulmonary disease using routine clinical data.Fujita medical journal · 2025Article
- Association analysis of sepsis progression to sepsis-induced coagulopathy: a study based on the MIMIC-IV database.BMC infectious diseases · 2025Article
- Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.Scientific reports · 2025Article
- Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model.Frontiers in cardiovascular medicine · 2025Article
- Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.Journal of inflammation research · 2025Article
- Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.Frontiers in immunology · 2025Article
- Septic Shock Requiring Three Vasopressors: Patient Demographics and Outcomes.Critical care explorations · 2024Article
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- Drug-induced coagulopathies: a real-world pharmacovigilance study using the FDA adverse event reporting system.Frontiers in pharmacology · 2024Article
- Association of serum phosphate and changes in serum phosphate with 28-day mortality in septic shock from MIMIC-IV database.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.