AIMC Topic: COVID-19

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Large-Scale Deep Learning-Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic.

JMIR infodemiology
BACKGROUND: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Di...

Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records.

BMC medical research methodology
BACKGROUND: Methods that enable early outbreak detection represent powerful tools in epidemiological surveillance, allowing adequate planning and timely response to disease surges. Syndromic surveillance data collected from primary healthcare encount...

The effect of home-based and remote exercises on low back pain during the COVID-19 pandemic: A systemic review.

Journal of bodywork and movement therapies
BACKGROUND: The prevalence of low back pain (LBP) surged during the COVID-19 pandemic, posing challenges to face-to-face treatment.

Prediction and Evaluation of Coronavirus and Human Protein-Protein Interactions Integrating Five Different Computational Methods.

Proteins
The high lethality and infectiousness of coronaviruses, particularly SARS-Cov-2, pose a significant threat to human society. Understanding coronaviruses, especially the interactions between these viruses and humans, is crucial for mitigating the coro...

Advancing Pedagogy in Leadership Practice to Enhance Public Health Impact.

Journal of public health management and practice : JPHMP
BACKGROUND: The COVID-19 pandemic highlighted the critical need for competent leadership in public health to address emergent health challenges and restore trust in health institutions. Emphasizing leadership training in public health education progr...

Changes in recreational drug use, reasons for those changes and their consequence during and after the COVID-19 pandemic in the UK.

Comprehensive psychiatry
Changes in drug use in the general population during the COVID-19 pandemic and their long-term consequences are not well understood. We employed natural language processing and machine learning to analyse a large dataset of self-reported rates of and...

Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.

ACS nano
Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpre...

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.

BMC infectious diseases
BACKGROUND: Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. ...

Population-level individualized prospective prediction of opioid overdose using machine learning.

Molecular psychiatry
The opioid overdose epidemic has rapidly expanded in North America, with rates accelerating during the COVID-19 pandemic. No existing study has demonstrated prospective opioid overdose at a population level. This study aimed to develop and validate a...

Remdesivir associated with reduced mortality in hospitalized COVID-19 patients: treatment effectiveness using real-world data and natural language processing.

BMC infectious diseases
BACKGROUND: Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (...