Emergency Medicine

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+...

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.

Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal per...

Clinicians' perspectives on the use of artificial intelligence to triage MRI brain scans.

Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tool...

Interpretable machine learning for predicting sepsis risk in emergency triage patients.

The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods...

Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.

INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, r...

Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study.

BACKGROUND: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are...

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...

Novel transfer learning based bone fracture detection using radiographic images.

A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by i...

From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning.

Human nails have recently become a sample of interest for toxicological purposes. Multiple studies have proven the ability to detect various analytes ...

Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation.

Carotid plaques-the buildup of cholesterol, calcium, cellular debris, and fibrous tissues in carotid arteries-can rupture, release microemboli into th...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.

Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic ...

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...

Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.

Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and...

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...

Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study.

BACKGROUND: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to ei...

Clinical utility of routine postoperative labs in emergency general surgery patients.

BACKGROUND: Morning postoperative labs are often obtained for emergency general surgery (EGS) patients. Studies in other surgical fields indicate that...

Diagnosis of traumatic liver injury on computed tomography using machine learning algorithms and radiomics features: The role of artificial intelligence for rapid diagnosis in emergency rooms.

BACKGROUND: The initial assessment of trauma is a time-consuming and challenging task. The purpose of this research is to examine the diagnostic effec...

A new distal radius fracture classification depending on the specific fragments through machine learning clustering method.

PURPOSES: The objective of this study was to investigate intra-articular distal radius fractures, aiming to provide a comprehensive analysis of fractu...

An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study.

BACKGROUND: Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for pat...