AIMC Topic: SARS-CoV-2

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Machine Learning Methods Based on Chest CT for Predicting the Risk of COVID-19-Associated Pulmonary Aspergillosis.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a machine learning model based on chest CT and clinical risk factors to predict secondary aspergillus infection in hospitalized COVID-19 patients.

Bias Amplification to Facilitate the Systematic Evaluation of Bias Mitigation Methods.

IEEE journal of biomedical and health informatics
The future of artificial intelligence (AI) safety is expected to include bias mitigation methods from development to application. The complexity and integration of these methods could grow in conjunction with advances in AI and human-AI interactions....

UnBias: Unveiling Bias Implications in Deep Learning Models for Healthcare Applications.

IEEE journal of biomedical and health informatics
The rapid integration of deep learning-powered artificial intelligence systems in diverse applications such as healthcare, credit assessment, employment, and criminal justice has raised concerns about their fairness, particularly in how they handle v...

Forecasting Epidemic Spread With Recurrent Graph Gate Fusion Transformers.

IEEE journal of biomedical and health informatics
Predicting the unprecedented, nonlinear nature of COVID-19 presents a significant public health challenge. Recent advances in deep learning, such as graph neural networks (GNNs), recurrent neural networks (RNNs), and Transformers, have enhanced predi...

Joint Energy-Based Model for Semi-Supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch.

IEEE journal of biomedical and health informatics
Semi-supervised learning effectively mitigates the lack of labeled data by introducing extensive unlabeled data. Despite achieving success in respiratory sound classification, in practice, it usually takes years to acquire a sufficiently sizeable unl...

Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.

BMC pulmonary medicine
BACKGROUND: Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.

Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.

Journal of biomedical informatics
OBJECTIVE: Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional dee...

Using ChatGPT for medical education: the technical perspective.

BMC medical education
BACKGROUND: The chatbot application Bennie and the Chats was introduced due to the outbreak of COVID-19, which is aimed to provide substitution for teaching conventional clinical history-taking skills. It was implemented with DialogFlow with preset r...

Systematic collection, annotation, and pattern analysis of viral vaccines in the VIOLIN vaccine knowledgebase.

Frontiers in cellular and infection microbiology
BACKGROUND: Viral vaccines have been proven significant in protecting us against viral diseases such as COVID-19. To better understand and design viral vaccines, it is critical to systematically collect, annotate, and analyse various viral vaccines a...

Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study.

JMIR formative research
BACKGROUND: The COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an "infodemic" of misinformation, particularly prevalent in ...