Pneumonia

Dysregulation of saliva and fecal microbiota as novel biomarkers of colorectal cancer.

The aim of this study was to investigate the biomarkers of salivary and fecal microbiota in Colorectal cancer (CRC). Initially, the study scrutinized the microbial community composition disparities among groups. Utilizing Lasso analysis, it sifted through operational taxonomic units (OTUs) to pinpoint distinctive features. Subsequently, by intersecting feature OTUs across groups, it curated a set ...

Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem.

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clini...

Policy brief: Improving national vaccination decision-making through data.

Life course immunisation looks at the broad value of vaccination across multiple generations, calling for more data power, collaboration, and multi-di...

ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists' intentions.

Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical...

Evaluating generative artificial intelligence's limitations in health policy identification and interpretation.

Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The ...

Novel concept for the healthy population influencing factors.

In the rapid urbanization process in China, due to reasons such as employment, education, and family reunification, the number of mobile population wi...

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.

The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty...

Flexibility and resilience in equity-centered research: lessons learned conducting a randomized controlled trial of a family-based substance use prevention program for American Indian families.

Meaningful and effective community engagement lies at the core of equity-centered research, which is a powerful tool for addressing health disparities...

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/...

Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study.

BACKGROUND: While deep learning classifiers have shown remarkable results in detecting chest X-ray (CXR) pathologies, their adoption in clinical setti...

MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-Ray Self-Supervised Representation Learning.

Self-supervised learning (SSL) reduces the need for manual annotation in deep learning models for medical image analysis. By learning the representati...

Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients.

The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transp...

Deanthropomorphising NLP: Can a language model be conscious?

This work is intended as a voice in the discussion over previous claims that a pretrained large language model (LLM) based on the Transformer model ar...

PhraseAug: An Augmented Medical Report Generation Model With Phrasebook.

Medical report generation is a valuable and challenging task, which automatically generates accurate and fluent diagnostic reports for medical images,...

Multi-Label Chest X-Ray Image Classification With Single Positive Labels.

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such dataset...

A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults.

Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction...

Lightweight convolutional neural network for chest X-ray images classification.

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a...

CorLabelNet: a comprehensive framework for multi-label chest X-ray image classification with correlation guided discriminant feature learning and oversampling.

Recent advancements in deep learning techniques have significantly improved multi-label chest X-ray (CXR) image classification for clinical diagnosis....

Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults.

UNLABELLED: Community mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining au...

Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural n...