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Domestic Violence

Latest AI and machine learning research in domestic violence for healthcare professionals.

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Identifying bladder rupture following traumatic pelvic fracture: A machine learning approach.

INTRODUCTION: Bladder rupture following blunt pelvic trauma is rare though can have significant sequ...

The use of machine learning techniques in trauma-related disorders: a systematic review.

Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Postt...

[Artificial Intelligence for the Development of Screening Parameters in the Field of Corneal Biomechanics].

Machine learning and artificial intelligence are mostly important if data analysis by knowledge-base...

Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement.

Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, locati...

Synthetic review of financial capacity in cognitive disorders: Foundations, interventions, and innovations.

PURPOSE OF REVIEW: Financial capacity (FC) is a complex, multi-dimensional construct that changes ov...

Building an Otoscopic screening prototype tool using deep learning.

BACKGROUND: Otologic diseases are often difficult to diagnose accurately for primary care providers....

Point-of-care cervical cancer screening using deep learning-based microholography.

Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures o...

DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction.

Detecting epistatic interaction is a typical way of identifying the genetic susceptibility of comple...

Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.

This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay...

Assessing the accuracy of machine-assisted abstract screening with DistillerAI: a user study.

BACKGROUND: Web applications that employ natural language processing technologies to support systema...

Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools.

BACKGROUND: We explored the performance of three machine learning tools designed to facilitate title...

Can artificial intelligence help identify elder abuse and neglect?

A health care encounter is a potentially critical opportunity to detect elder abuse and initiate int...

Liver disease screening based on densely connected deep neural networks.

Liver disease is an important public health problem. Liver Function Tests (LFT) is the most achievab...

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance.

Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions....

Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening.

Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand act...

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead ele...

Development and validation of deep learning algorithms for scoliosis screening using back images.

Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence ...

Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

BACKGROUND: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved w...

Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.

BACKGROUND: Current lung cancer screening guidelines use mean diameter, volume or density of the lar...

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