Surveillance

Machine learning and deep learning tools for the automated capture of cancer surveillance data.

The National Cancer Institute and the Department of Energy strategic partnership applies advanced co...

The role of artificial intelligence-assisted endoscopy surveillance in clinical practice: controversies and future perspectives.

Surveillance of Health Care-Associated Violence Using Natural Language Processing.

BACKGROUND AND OBJECTIVES: Patient and family violent outbursts toward staff, caregivers, or through...

[Application of the blastomere count variations "skip value" in the embryo AI assessment].

To explore the correlation between blastomere count variations "skip value" which extracted from by...

Precision Nutrition Management in Continuous Care: Leveraging AI for User-Reported Dietary Data Analysis.

A As health technology advances, this study aims to develop an innovative nutritional intake managem...

Predicting Diabetes in Canadian Adults Using Machine Learning.

Rising diabetes rates have led to increased healthcare costs and health complications. An estimated ...

Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting.

BACKGROUND: Early medical attention after concussion may minimize symptom duration and burden; howev...

Use of artificial intelligence for health care-associated infection surveillance: Correspondence.

Response to the letter to the editor regarding "Assisting the Infection Preventionist: Use of Artificial Intelligence for Healthcare-Associated Infection Surveillance".

Advancing radiology reporting with large language models: Is GPT-4 the LI-RADS game changer or just a wild card?

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE...

Harnessing causal forests for epidemiologic research: key considerations.

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent in...

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

BACKGROUND: Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (G...

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based method...

Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review.

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical prac...

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowle...

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms rang...

How Well Do AI-Enabled Decision Support Systems Perform in Clinical Settings?

Real-world performance of machine learning (ML) models is crucial for safely and effectively embeddi...

DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology.

Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Da...

Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.

Integrating Electronic Health Records (EHR) and the application of machine learning present opportun...

Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of he...