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An Interpretable Machine Learning Model for Early Multitemporal Prediction of Onset of Acute Kidney Injury in Intensive Care Unit Patients with Severe Trauma.

Background: Acute Kidney Injury (AKI), a leading organ failure cause in critical patients, demands early high-risk identification to enhance outcomes. Yet comparative analyses of diagnostic and prognostic machine learning (ML) models across multiple post-admission timeframes are lacking. Methods: Using MIMIC-IV, we carried out using the Boruta algorithm for feature selection, developing and compar...

Augmenting decision making in acute care surgery: A systematic review of machine learning-driven risk prediction models.

BACKGROUND: Acute care surgery (ACS) involves rapid, high-stakes decisions with limited opportunity for preoperative planning. While machine learning (ML) may improve risk prediction and decision making in this setting, its development, validation, and implementation in ACS remain understudied. We therefore evaluated the techniques, predictor features, and outcomes used in ML-driven risk predictio...

Performance of machine learning algorithms in predicting the need for surgical fixation in pediatric craniomaxillofacial trauma.

Machine learning offers a novel approach to improve surgical triage in pediatric craniomaxillofacial trauma, where decision-making often relies on cli...

The Performance of Artificial Intelligence in Providing Real-Time Aid in Emergency Dental Trauma: A Clinical Validation Study.

BACKGROUND: Searching online for dental emergency treatment as a non-expert can lead to unreliable guidance. We tested the publicly available first mu...

Toward Standardized ICU Trauma Resuscitation via Markov Decision Processes.

OBJECTIVE: Guideline-based recommendations for posthemostasis resuscitation in trauma patients remain limited. This study aimed to define an interpret...

Graph Neural Networks for Realistic Bleeding Prediction in Surgical Simulators.

This study presents a novel approach using graph neural networks to predict the risk of internal bleeding using vessel maps derived from patient CT an...

From prediction to precision: The emerging impact of artificial intelligence in trauma care.

Diagnostic technologies for neuroblastoma.

Neuroblastoma is an aggressive childhood cancer characterised by high relapse rates and heterogenicity. Current medical diagnostic methods involve an ...

Rapid trauma classification under data scarcity: an emergency on-scene decision model combining natural language processing and machine learning.

Trauma has become a major cause of increased morbidity and mortality worldwide. In emergency response, the classification of injuries is crucial as it...

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

Despite advancements in trauma care, uncontrolled hemorrhage and trauma-induced coagulopathy (TIC) remain the leading causes of preventable deaths aft...

Promising for patients or deeply disturbing? The ethical and legal aspects of deepfake therapy.

Deepfakes are hyper-realistic but fabricated videos created with the use of artificial intelligence. In the context of psychotherapy, the first studie...

Improving prediction of fragility fractures in postmenopausal women using random forest.

Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher...

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in ...

AI in Learning Anatomy and Restoring Central Incisors: A Comparative Study.

More than 1 billion individuals worldwide have experienced dental trauma, particularly children aged 7 to 12 y, predominantly affecting the anterior t...

Natural orifice transluminal endoscopic surgery: history and current development.

Natural orifice transluminal endoscopic surgery (NOTES) represents an innovative advancement in minimally invasive surgery, utilizing natural body ori...

Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics.

BACKGROUND: In clinical work, there are difficulties in distinguishing pulmonary contusion(PC) from bacterial pneumonia(BP) on CT images by the naked ...

Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study.

BACKGROUND: Substance use disorder (SUD) involves excessive substance consumption and persistent reward-seeking behaviors, leading to serious physical...

Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury.

Traumatic brain injury (TBI) is a major public health concern associated with an increased risk of neurodegenerative diseases including Alzheimer's di...

Adolescent lumbar disc herniation: etiology, diagnosis, and treatment options.

Adolescent lumbar disc herniation (ALDH) is a type of disease with a much lower incidence than adult lumbar disc herniation (LDH), which has a trend o...

Pediatric Maxillofacial Trauma: Machine Learning Based Predictive Modeling to Identify Trauma Patterns.

AIM: To analyze the characteristics of pediatric facial trauma and predict factors influencing them through machine learning algorithms.