Domestic Violence

Design optimization and predictive modeling for TSS in mega surface WWTPs: A machine learning approach.

Surface wastewater originates from various sources, including domestic, commercial, and industrial activities, and contains a mix of organic and inorganic wastes along with suspended and dissolved solids that require effective treatment. This study proposes a treatment process design for surface wastewater, utilizing unit-by-unit mass balances for flow rates (Q), total suspended solids (TSS), and ...

Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs.

The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A system that enhances the diagnostic efficacy of head CT imaging in emergency settings through structured reporting would significantly improve clinical decision making. Currently, no AI solutions address this need. Thus, our research aims to develop ...

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...

A Comparison of Prostate Cancer Screening Information Quality on Standard and Advanced Versions of ChatGPT, Google Gemini, and Microsoft Copilot: A Cross-Sectional Study.

PurposeArtificially Intelligent (AI) chatbots have the potential to produce information to support shared prostate cancer (PrCA) decision-making. Ther...

Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography.

Non-enhanced head computed tomography is widely used for patients presenting with head trauma or stroke, given acute intracranial hemorrhage significa...

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 ...

Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.

PURPOSE: The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging re...

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...

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We...

MyEcoReporter: a prototype for artificial intelligence-facilitated pollution reporting.

BACKGROUND: Many chemical releases are first noticed by community members, but reporting these concerns often involves considerable hurdles. Artificia...

Cell clone selection-impact of operation modes and medium exchange strategies on clone ranking.

Bioprocessing has been transitioning from batch to continuous processes. As a result, a considerable amount of resource was dedicated to optimising st...

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 ...

Machine Learning-Assisted High-Throughput Screening of Nanozymes for Ulcerative Colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature...

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...

AI image analysis as the basis for risk-stratified screening.

Artificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with two distinct applications: computer-aided cancer de...

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...