ArticleFrontiers in medicine2021
An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.BMC medical informatics and decision making · 2026Article
- Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia.Current opinion in anaesthesiology · 2026Review
- Advancing precision triage in strangulated small bowel obstruction: from static scores to dynamic multiparametric models.Frontiers in surgery · 2026Review
- [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
- Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.Frontiers in immunology · 2025Article
- From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases.BioData mining · 2024Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8
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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.