Sexual Assault

Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.

Approximately 70% of individuals globally experience at least one traumatic event in their lifetimes, potentially leading to posttraumatic stress disorder (PTSD). Understanding the development of PTSD and devising effective prevention and treatment strategies are crucial. This proof-of-concept study aimed to design a concise tree-based adaptive test using the Classification and Regression Trees (...

A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study.

BACKGROUND: Early treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in patients with thoracic trauma.

RespBERT: A Multi-Site Validation of a Natural Language Processing Algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS).

Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction associated with significant mortality and morbidity among critically ill pati...

Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.

BACKGROUND: Vertebral compression fractures (VCFs) are prevalent in the elderly, often caused by osteoporosis or trauma. Differentiating acute from ch...

A comparative analysis of trauma-related mortality in South Korea using classification models.

BACKGROUND: Reducing mortality among severe trauma patients requires the establishment of an effective emergency transportation system and the rapid t...

A novel clinical investigation using deep learning and human-in-the-loop approach in orbital volume measurement.

Orbital volume assessment is crucial for surgical planning. Traditional methods lack efficiency and accuracy. Recent studies explore AI-driven techniq...

AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.

To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings ...

Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and midd...

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in div...

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

BACKGROUND: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trau...

Predicting fall parameters from infant skull fractures using machine learning.

When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limite...

Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.

OBJECTIVES: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medic...

Novel Machine-Learning Modeling of Facial Trauma Volume With Regional Event and Weather Data.

OBJECTIVE: Facial trauma volume is difficult to predict accurately. We aim to understand the capacity of climate and regional events to predict daily ...

Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.

AIM: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of mach...

Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demog...

External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.

Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI ...

Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females.

Childhood abuse represents one of the most potent risk factors for the development of psychopathology during childhood, accounting for 30-60% of the r...

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving o...

Forecasting Pediatric Trauma Volumes: Insights From a Retrospective Study Using Machine Learning.

INTRODUCTION: Rising pediatric firearm-related fatalities in the United States strain Trauma Centers. Predicting trauma volume could improve resource ...

Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.

OBJECTIVE: Trauma resuscitation is the initial evaluation and management of injured patients in the emergency department. This time-critical process r...