ReviewExperimental hematology & oncology2024
Genetic factors, risk prediction and AI application of thrombotic diseases.
Review in Experimental hematology & oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multimodal AI and single-cell transcriptomics integrate to construct a histopathological prognostic model for bladder cancer, revealing the RTN3-glycolysis axis in chemoresistance.Experimental hematology & oncology · 2026Article
- Biology-informed risk stratification of glioblastoma by integrating MRI-based intratumoral heterogeneity with clinical features: a multicenter validation study.Experimental hematology & oncology · 2026Article
- Cytogenetic alterations and coagulopathy in breast cancer patients: molecular mechanisms and clinical implications.Annals of medicine and surgery (2012) · 2026Review
- [Development and Validation of a Risk Prediction Model for Secondary Pulmonary Infarction in Elderly Patients With Acute Pulmonary Embolism].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Acute Non-Hemorrhagic Adrenal Infarction in a Diabetic Patient: A Rare but Critical Consideration in Acute Abdominal Pain.Clinical case reports · 2025Article
- Machine learning for predicting thrombotic recurrence in antiphospholipid syndrome.Research and practice in thrombosis and haemostasis · 2025Article
- Establishing altitude-based coagulation reference ranges in Western Sichuan.Scientific reports · 2025Article
- Research trends in essential thrombocythemia from 2001 to 2024: a bibliometric analysis.Discover oncology · 2025Article
- The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia.Bioengineering (Basel, Switzerland) · 2025Article
- Catastrophic asparaginase-induced cerebral and systemic thrombosis in a young female with T-cell lymphoblastic lymphoma: A case report & literature review.Leukemia research reports · 2025Article
- Navigating antiphospholipid syndrome: from personalized therapies to cutting-edge research.Rheumatology advances in practice · 2025Review
- Dietary Antioxidants and Natural Compounds in Preventing Thrombosis and Cardiovascular Disease.International journal of molecular sciences · 2024Review
- Clinical Impact of Inherited Thrombophilia in Patients With Thrombosis: Evaluating the Real Risk of FVL, PTG20210A, and MTHFR Mutations in the Kurdistan Region of Iraq.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
3 authors.
Funding
Abstract
In thrombotic diseases, coagulation, anticoagulation, and fibrinolysis are three key physiological processes that interact to maintain blood in an appropriate state within blood vessels. When these processes become imbalanced, such as excessive coagulation or reduced anticoagulant function, it can lead to the formation of blood clots. Genetic factors play a significant role in the onset of thrombotic diseases and exhibit regional and ethnic variations. The decision of whether to initiate prophylactic anticoagulant therapy is a matter that clinicians must carefully consider, leading to the development of various thrombotic risk assessment scales in clinical practice. Given the considerable heterogeneity in clinical diagnosis and treatment, researchers are exploring the application of artificial intelligence in medicine, including disease prediction, diagnosis, treatment, prevention, and patient management. This paper reviews the research progress on various genetic factors involved in thrombotic diseases, analyzes the advantages and disadvantages of commonly used thrombotic risk assessment scales and the characteristics of ideal scoring scales, and explores the application of artificial intelligence in the medical field, along with its future prospects.
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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 Socratic graph.