Domestic Violence

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a pop...

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

OBJECTIVE: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening using low-dose chest computed tomography (LDCT) scans obtained for lung cancer screening.

Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening.

Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional informati...

Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.

BACKGROUND AND OBJECTIVE: Performing patient-specific, pre-operative cochlea CT-based measurements could be helpful to positively affect the outcome o...

A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules - Proof of concept study using an artificial neural network for sample classification.

Liquid chromatography coupled to high-resolution mass spectrometry (HRMS) enables data independent acquisition (DIA) and untargeted screening. However...

Factors Influencing Delivery of Cancer Survivorship Care Plans: A National Patterns of Care Study.

Nearly half of all Americans will develop cancer at least once in their lifetime. Through improved screening and treatments, the number of cancer surv...

Automatic detection of rare pathologies in fundus photographs using few-shot learning.

In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, ...

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population.

In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to unde...

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials.

Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, ...

Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performanc...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Com...

Evaluation of the predictive ability of ultrasound-based assessment of breast cancer using BI-RADS natural language reporting against commercial transcriptome-based tests.

PURPOSE: The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feat...

Stand-alone artificial intelligence - The future of breast cancer screening?

Although computers have had a role in interpretation of mammograms for at least two decades, their impact on performance has not lived up to expectati...

International evaluation of an AI system for breast cancer screening.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful. Despite the existence of...

Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.

The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underw...

On-site testing of multiple drugs of abuse in urine by a miniature dual-LIT mass spectrometer.

There is an increasing need for rapid and on-site detection of emerging drugs of abuse. In this work, we developed a method using a miniature dual-LIT...

Medication-rights detection using incident reports: A natural language processing and deep neural network approach.

Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and...

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

INTRODUCTION: Bladder rupture following blunt pelvic trauma is rare though can have significant sequelae. We sought to determine whether machine learn...

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 Posttraumatic Stress Disorder (PTSD) have always been a...

Twin Robotic X-Ray System for 3D Cone-Beam CT of the Wrist: An Evaluation of Image Quality and Radiation Dose.

The purpose of this study was to assess image quality and radiation dose of a novel twin robotic x-ray system's 3D cone-beam CT (CBCT) function for t...