Surveillance

Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types.

Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk prediction models have played a crucial role in epidemiology of several cancer types, they have limitations especially in the ability to process complex and multidimensional data. In contrast, artificial intelligence (AI) approaches represent a promising solu...

Explainable machine learning for predicting lung metastasis of colorectal cancer.

Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model is paramount. Our study seeks to construct and verify a predictive model utilizing machine learning (ML) that can evaluate the risk of lung metastasis with newly diagnosed colorectal cancer (CRC) using Shapley Additive exPlanations (SHAP). Using the...

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model.

Lymph node status is a critical prognostic predictor for patients; however, the prognosis of colorectal signet-ring cell carcinoma (SRCC) has garnered...

DeepGuard: real-time threat recognition using Golden Jackal optimization with deep learning model.

Violence recognition in surveillance videos is a vital feature of current security systems. It can improve complete security measures, making it essen...

Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records.

BACKGROUND: Methods that enable early outbreak detection represent powerful tools in epidemiological surveillance, allowing adequate planning and time...

Transfer Learning Enhanced Blood Pressure Monitoring Based on Flexible Optical Pulse Sensing Patch.

Blood pressure (BP), a crucial health biomarker, is essential for detecting early indications of cardiovascular disease in routine monitoring and clin...

Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study.

BACKGROUND: Unlike one-snap data collection methods that only identify high-risk patients, machine learning models using time-series data can predict ...

Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic characteristics.

Viral codon fitness (VCF) of the host and the VCF shifting has seldom been studied under quantitative measurements, although they could be concepts vi...

Low-cost algorithms for clinical notes phenotype classification to enhance epidemiological surveillance: A case study.

OBJECTIVE: Our study aims to enhance epidemic intelligence through event-based surveillance in an emerging pandemic context. We classified electronic ...

Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting.

Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical ...

A comparison of neural networks and regression-based approaches for estimating kidney function in pediatric chronic kidney disease: Practical predictive epidemiology for clinical management of a progressive disease.

PURPOSE: Clinical management of pediatric chronic kidney disease requires estimation of glomerular filtration rate (eGFR). Currently, eGFR is determin...

Precision in Prevention and Health Surveillance: How Artificial Intelligence May Improve the Time of Identification of Health Concerns through Social Media Content Analysis.

OBJECTIVE: To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance "precisio...

A neural network approach to glomerular filtration rate estimation: a single-centre retrospective audit.

OBJECTIVES: The 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race correction factor is frequently used for an est...

Improving the usability of large emergency 911 data reporting systems: A machine learning case study using emergency incident descriptions.

INTRODUCTION: Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fir...

Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients.

Muscle-Invasive Bladder Cancer (MIBC) is a more aggressive disease than non-muscle-invasive bladder cancer (NMIBC), with greater chances of metastasis...

Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects.

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and a...

Validation of a generative artificial intelligence tool for the critical appraisal of articles on the epidemiology of mental health: Its application in the Middle East and North Africa.

Mental health disorders have a high disability-adjusted life years in the Middle East and North Africa. This rise has led to a surge in related public...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in...

Multimodal learning-based speech enhancement and separation, recent innovations, new horizons, challenges and real-world applications.

With the increasing global prevalence of disabling hearing loss, speech enhancement technologies have become crucial for overcoming communication barr...

Cholera resurges in Zambia: Challenges and future directions.

Cholera remains a major global health challenge, disproportionately affecting low-income countries with inadequate water, sanitation, and hygiene (WAS...