Public Health

CAD-Unet: A capsule network-enhanced Unet architecture for accurate segmentation of COVID-19 lung infections from CT images.

Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accurate quantification and diagnosis of COVID-19. Segmentation of COVID-19 infections in the lungs poses a formidable challenge, primarily due to the ind...

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...

Vaccinology in the artificial intelligence era.

Artificial intelligence (AI) has already transformed vaccine antigen design and could transform the entire vaccinology pipeline, including immune resp...

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...

Analyzing the impact of COVID-19 on seasonal infectious disease outbreak detection using hybrid SARIMAX-LSTM model.

BACKGROUND: This study estimates the incidence of seasonal infectious diseases, including influenza, norovirus, severe fever with thrombocytopenia syn...

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 ...

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 vaccine chatbot intervention for parents to improve HPV vaccination uptake among middle school girls: a cluster randomized trial.

Conversational artificial intelligence, in the form of chatbots powered by large language models, offers a new approach to facilitating human-like int...

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...

The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses.

This paper discusses the responsible use of artificial intelligence (AI) in public health and in medicine, and questions the development of AI ethics ...

Refining early detection of Marburg Virus Disease (MVD) in Rwanda: Leveraging predictive symptom clusters to enhance case definitions.

BACKGROUND: Marburg Virus Disease (MVD) poses a significant global health risk due to its high case fatality rates (24%-88%) and the diagnostic challe...

Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species.

Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-speci...

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...

The generative revolution: AI foundation models in geospatial health-applications, challenges and future research.

In an era of rapid technological advancements, generative artificial intelligence and foundation models are reshaping industries and offering new adva...

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...

Identifying chemotherapy beneficiaries in nasal and paranasal sinus cancers: epidemiological trends and machine learning insights.

BACKGROUND: Studies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relativel...