BACKGROUND: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratifica...
Available classification tools and risk factors predicting bleeding events in elderly patients after mechanical valve replacement may not be suitable in Asian populations. Thus, we aimed to identify an accurate model for predicting bleeding in elderl...
Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR are the hemorrhages in the retina. Therefore, we pro...
The journal of trauma and acute care surgery
34016929
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome ...
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...
The American journal of forensic medicine and pathology
33833193
Convolutional neural network (CNN) has advanced in recent years and translated from research into medical practice, most notably in clinical radiology and histopathology. Research on CNNs in forensic/postmortem pathology is almost exclusive to postmo...
The journal of trauma and acute care surgery
35271546
Hemorrhagic shock remains the leading cause of mortality in civilian trauma and battlefield settings. The ability of combat medics and other military medical personnel to obtain early identification and assessment of a bleeding casualty is hampered b...