Domestic Violence

Achieving Equity with Predictive Policing Algorithms: A Social Safety Net Perspective.

Whereas using artificial intelligence (AI) to predict natural hazards is promising, applying a predictive policing algorithm (PPA) to predict human threats to others continues to be debated. Whereas PPAs were reported to be initially successful in Germany and Japan, the killing of Black Americans by police in the US has sparked a call to dismantle AI in law enforcement. However, although PPAs may ...

Predicting youth diabetes risk using NHANES data and machine learning.

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performan...

Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review.

cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk...

Reporting guidelines for artificial intelligence in healthcare research.

Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when repor...

Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies.

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and e...

Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography.

Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung...

Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs.

We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnost...

Impact of the Implementation of the EAU Guidelines Recommendation on Reporting and Grading of Complications in Patients Undergoing Robot-assisted Radical Cystectomy: A Systematic Review.

In 2012, the European Association of Urology (EAU) Ad Hoc Panel proposed a standardised methodology on reporting and grading complications after urolo...

Automatic Detection of Thyroid and Adrenal Incidentals Using Radiology Reports and Deep Learning.

BACKGROUND: Computed tomography (CT) is commonly performed when evaluating trauma patients with up to 55% showing incidental findings. Current workflo...

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. ...

[Main biological tools applied to newborn screening: Landscape and future perspectives].

Over the past fifty years, neonatal screening has become essential in the public health programs of a large number of countries. During all these year...

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analy...

Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification.

Rib fractures are injuries commonly assessed in trauma wards. Deep learning has demonstrated state-of-the-art accuracy for a variety of tasks, includi...

Breast cancer risk prediction in African women using Random Forest Classifier.

INTRODUCTION: One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic...

Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma.

Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is...

A Multiprocessing Scheme for PET Image Pre-Screening, Noise Reduction, Segmentation and Lesion Partitioning.

Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, st...

Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model.

BACKGROUND: Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. Howev...

Artificial intelligence for adult spinal deformity: current state and future directions.

As we experience a technological revolution unlike any other time in history, spinal surgery as a discipline is poised to undergo a dramatic transform...

Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions.

Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage...

Machine learning analysis: general features, requirements and cardiovascular applications.

Artificial intelligence represents the science which will probably change the future of medicine by solving actually challenging issues. In this speci...