AIMC Topic: Blood Coagulation Disorders

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Coagulation Risk Prediction in Patients With Liver Failure: Integrated Meta-Analysis and Machine Learning Model Study.

JMIR medical informatics
BACKGROUND: Liver failure often results in significant coagulation dysfunction, which is a major complication. Artificial liver support systems (ALSS) have been used to ameliorate coagulation parameters, but the dynamic nature of these improvements a...

Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting.

Emergency medicine journal : EMJ
BACKGROUND: Early intervention in patients with major traumatic injuries is critical. Decision support can improve clinicians' ability to identify high-risk patients. The aim of this study was to compare the performance of a machine-learning (ML) dec...

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy.

Scientific reports
Despite advancements in trauma care, uncontrolled hemorrhage and trauma-induced coagulopathy (TIC) remain the leading causes of preventable deaths after trauma. Understanding the genetic underpinnings and molecular mechanisms of TIC is crucial for de...

Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.

Frontiers in immunology
BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.

Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study.

Journal of medical Internet research
BACKGROUND: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic morta...

A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients.

The journal of trauma and acute care surgery
BACKGROUND: Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusi...

Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: MRI-only planning relies on dosimetrically accurate synthetic-CT (sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of sCTs generated using a deep learning algorithm for pelvic, brain and hea...

Low-soluble TREM-like transcript-1 levels early after severe burn reflect increased coagulation disorders and predict 30-day mortality.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: Patients with severe burns often show systemic coagulation changes in the early stage and even develop extensive coagulopathy. Previous studies have confirmed that soluble TREM-like transcript-1 (sTLT-1) mediates a novel mechanism of haem...

Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.

CNS neuroscience & therapeutics
AIMS: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate...