Surveillance

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 reporting a study. The use of artificial intelligence (AI) reporting guidelines that address potential sources of bias specific to studies involving AI interventions has the potential to improve the quality of AI studies, through improvements in their des...

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 eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow imp...

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...

Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.

Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of d...

Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed.

Rare diseases affect between 25 and 30 million people in the United States, and understanding their epidemiology is critical to focusing research effo...

Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal.

Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics...

A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method.

Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. Th...

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...

Artificial intelligence in oncology: Path to implementation.

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety o...

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...

High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors.

The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbrea...

Adaptive Floor Cleaning Strategy by Human Density Surveillance Mapping with a Reconfigurable Multi-Purpose Service Robot.

Professional cleaning and safe social distance monitoring are often considered as demanding, time-consuming, repetitive, and labor-intensive tasks wit...

Charting a path forward for clinical research in artificial intelligence and gastroenterology.

Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, an...

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.

Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed ...

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore...

Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.

OBJECTIVES: To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological ca...

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos.

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is ...

A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things.

BACKGROUND: As a neglected cross-species parasitic disease transmitted between canines and livestock, echinococcosis remains a global public health co...

Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas.

The world's oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosyst...