Latest AI and machine learning research in public health for healthcare professionals.
This paper focuses on how machine learning (ML) algorithms and applications have been used to analyze disease severity and mortality prediction in COVID-19 research. In the past, simpler statistical and epidemiological methods were more commonly used by researchers and officials to predict the course of the pandemic. However, in recent years, the limitations, high costs, and time required for medi...
Increasingly, research suggests that aging is a coordinated multi-system decline in functioning that occurs at multiple biological levels. We developed and validated a transcriptomic (RNA-based) aging measure we call Transcriptomic Mortality-risk Age (TraMA) using RNA-seq data from the 2016 Health and Retirement Study using elastic net Cox regression analyses to predict 4-year mortality hazard. In...
Clinical management and surveillance of the complex (ECC) face significant challenges due to inaccurate species identification and prolonged turnarou...
Background and study aims Prophylactic total gastrectomy (PTG) is the definitive treatment for hereditary diffuse gastric cancer syndrome (HDGC). Endo...
The perspective review discusses predictive stability computational modeling and scientific risk-based approaches to prospectively assess long-term st...
UNLABELLED: serotyping is essential for epidemiological studies and clinical treatment guidance. However, traditional serological agglutination metho...
Cervical cancer remains a leading cause of cancer-related death among women globally, despite the availability of effective prevention through human p...
Developing vaccines with a better stability is an area of improvement to meet the global health needs of preventing infectious diseases. With the adva...
Systematic literature review (SLR) is an important tool for Health Economics and Outcomes Research (HEOR) evidence synthesis. SLRs involve the identif...
This study investigates public sentiment toward COVID-19 vaccinations by analyzing Twitter data using advanced machine learning (ML) and natural langu...
Emerging and re-emerging infectious diseases (EIDs and Re-EIDs) cause significant economic crises and public health problems worldwide. Epidemics app...
Satellite data have long been recognized as valuable for air quality applications. These applications are in a stage of rapid growth: new geostationar...
The Asia-Pacific region faces serious liver health challenges, primarily because of the comparatively high prevalence of hepatitis B virus (HBV) and h...
Vaccines have eradicated deadly diseases, yet vaccine hesitancy persists, leading to reduced uptake. Some individuals, mistrustful of healthcare prov...
As a major worldwide health concern, influenza still requires precise modeling of flu dynamics and efficient treatment approaches. Deep learning archi...
Accurate identification of sandfly species is critical for controlling and preventing the spread of visceral leishmaniasis, a major public health conc...
BACKGROUND: Despite decades of research on the influenza virus, we still lack a predictive understanding of how vaccination reshapes each person's ant...
Rickettsia is a genus of bacteria that are obligate intracellular parasites and are responsible for the febrile diseases known collectively as Rickett...
Ebola virus disease (EVD) is an acute life-threatening disease caused by highly pathogenic Ebolavirus (EBOV), with reported case fatality rates reachi...
The rapid emergence and evolution of infectious pathogens, including the COVID-19 pandemic and recurring influenza outbreaks, underscore the need for ...