AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Pandemics

Showing 101 to 110 of 750 articles

Clear Filters

Predicting COVID-19 pandemic waves including vaccination data with deep learning.

Frontiers in public health
INTRODUCTION: During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, an...

Effects of User-Reported Risk Factors and Follow-Up Care Activities on Satisfaction With a COVID-19 Chatbot: Cross-Sectional Study.

JMIR mHealth and uHealth
BACKGROUND: The COVID-19 pandemic influenced many to consider methods to reduce human contact and ease the burden placed on health care workers. Conversational agents or chatbots are a set of technologies that may aid with these challenges. They may ...

An adaptive ensemble deep learning framework for reliable detection of pandemic patients.

Computers in biology and medicine
Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The ...

Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic.

PloS one
BACKGROUND: Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cas...

Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review.

International journal of medical informatics
INTRODUCTION: Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of t...

Detection of safety helmet and mask wearing using improved YOLOv5s.

Scientific reports
With the advancement of society, ensuring the safety of personnel involved in municipal construction projects, particularly in the context of pandemic control measures, has become a matter of utmost importance. This paper introduces a security measur...

Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture.

Computers in biology and medicine
The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initi...

Spirometry services in England post-pandemic and the potential role of AI support software: a qualitative study of challenges and opportunities.

The British journal of general practice : the journal of the Royal College of General Practitioners
BACKGROUND: Spirometry services to diagnose and monitor lung disease in primary care were identified as a priority in the NHS Long Term Plan, and are restarting post-COVID-19 pandemic in England; however, evidence regarding best practice is limited.

Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.

The Lancet. Digital health
BACKGROUND: Deep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are ...

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

Frontiers in immunology
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic reg...