Surveillance

The effect of the pandemic on European narratives on smart cities and surveillance.

This article presents an analysis of European smart city narratives and how they evolved under the pressure of the COVID-19 pandemic. We start with Joss et al.'s observation that the smart-city discourse is presently in flux, engaged in intensive boundary-work and struggling to gain wider support. We approach this process from the critical perspective of surveillance capitalism, as proposed by Zub...

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era.

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, includin...

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.

Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormaliti...

A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays.

BACKGROUND: Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed, a...

Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review.

An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical informat...

Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution.

BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-sc...

An introduction to causal inference for pharmacometricians.

As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and ...

Artificial intelligence in diagnostic and interventional radiology: Where are we now?

The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI)...

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators.

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implemen...

Spatio-temporally smoothed deep survival neural network.

The analysis of registry data has important implications for cancer monitoring, control, and treatment. In such analysis, (semi)parametric models, suc...

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an inciden...

Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.

OBJECTIVES: To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting...

An autonomous decision-making framework for gait recognition systems against adversarial attack using reinforcement learning.

Gait identification based on Deep Learning (DL) techniques has recently emerged as biometric technology for surveillance. We leveraged the vulnerabili...

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.

Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this s...

Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre.

Rapid detection of intracranial haemorrhage (ICH) is crucial for assessing patients with neurological symptoms. Prioritising these urgent scans for re...

Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning.

BACKGROUND: Cardiac implantable electronic device (CIED) surgical site infections (SSIs) have been outpacing the increases in implantation of these de...

Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis.

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm a...

Generalizing Correspondence Analysis for Applications in Machine Learning.

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embe...

Combining staged laparoscopic colectomy with robotic completion proctectomy and ileal pouch-anal anastomosis (IPAA) in ulcerative colitis for improved clinical and cosmetic outcomes: a single-center feasibility study and technical description.

Robotic proctectomy has been shown to lead to better functional outcomes compared to laparoscopic surgery in rectal cancer. However, in ulcerative col...

Framework for Vehicle Make and Model Recognition-A New Large-Scale Dataset and an Efficient Two-Branch-Two-Stage Deep Learning Architecture.

In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation S...