A5018410896- COVID-19 and Mental Health
- COVID-19 diagnosis using AI
- COVID-19 Clinical Research Studies
- Blind Source Separation Techniques
- Long-Term Effects of COVID-19
- Digital Mental Health Interventions
- EEG and Brain-Computer Interfaces
- Telemedicine and Telehealth Implementation
- Cardiac Arrest and Resuscitation
- Artificial Intelligence in Healthcare
- IoT and Edge/Fog Computing
- Cardiac Structural Anomalies and Repair
- AI in cancer detection
- Context-Aware Activity Recognition Systems
- Neural Networks and Applications
- Social Media in Health Education
- SARS-CoV-2 and COVID-19 Research
- SARS-CoV-2 detection and testing
- Mechanical Circulatory Support Devices
- COVID-19 and healthcare impacts
- Advanced Memory and Neural Computing
- COVID-19 epidemiological studies
- Big Data and Business Intelligence
- Artificial Intelligence in Healthcare and Education
- Colorectal Cancer Screening and Detection
Vidant Health
2025
Clarkson University
2023
Virginia Commonwealth University
2022
Shell (Netherlands)
2022
George Mason University
2022
Creative Commons
2022
Hartford Hospital
2013
<title>Abstract</title> Colorectal cancer (CRC) is the 2nd leading cause of death in United States (US). Rural Appalachia suffers highest CRC incidence and mortality rates. There are several non-clinical health-related social determinant factors (SDOH) associated with mortality. This study describes novel predictive modeling that uses demographic, clinical, SDOH features from health records data Appalachian community centers to predict 5-year survival. We trained, validated, tested four...
The COVID-19 pandemic has affected people's lives beyond severe and long-term physical health symptoms. Social distancing quarantine have led to adverse mental outcomes. COVID-19-induced economic setbacks also likely exacerbated the psychological distress affecting broader aspects of well-being. Remote digital studies can provide information about pandemic's socioeconomic, mental, impact. COVIDsmart was a collaborative effort deploy complex research study understand impact on diverse...
patient with RV infarction cardiogenic shock refractory to standard care.
Background and Objective: At-home rapid antigen tests provide a convenient expedited resource to learn about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection status. However, low sensitivity of at-home presents challenge. This study examines the accuracy tests, when combined with computer-facilitated symptom screening. Methods: The used primary data sources collected during phases at different periods (phase 1 phase 2): one period in which alpha variant SARS-CoV-2 was...
Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that COVID-19 test kits have high rate of false negatives. One way to improve the accuracy acceptance screening is combine existing physical with an easily accessible, electronic, self-diagnostic tool. The objective current study was acceptability usability artificial intelligence (AI)-enabled tool combines web-based symptom diagnostic...
Abstract Background Decentralized, digital health studies can provide real-world evidence of the lasting effects COVID-19 on physical, socioeconomic, psychological, and social determinant factors in India. Existing research cohorts, however, are small were not designed for longitudinal collection comprehensive data from India’s diverse population. Data4Life is a nationwide, digitally enabled, initiative to examine post-acute sequelae across individuals, communities, regions. seeks build an...
Due to the large interest and need, there has been much recent work in epileptic seizure detection using machine learning models. Using un-intrusive measurements of brain activity such as electroencephalograms (EEG) allowed for datasets be constructed used computational intelligence identify events within EEG data. In this paper, we use a publicly avaibale dataset develop lightweight Machine supervised model (simple Decision Tree) classify from waves. The performance developed was compared...
Because of the substantial interest and necessity, there has been much recent work in epileptic seizure detection using machine learning models. U sing un-intrusive measurements brain activity such as electroencephalograms (EEG) allowed for large datasets to be constructed used computational intelligence identify events within EEG data. In this paper, we use a publicly available dataset develop lightweight Machine-learning supervised model (simple Decision Tree) classify from waves. The...
<sec> <title>BACKGROUND</title> The COVID-19 pandemic has affected people's lives beyond severe and long-term physical health symptoms. Social distancing quarantine have led to adverse mental outcomes. COVID-19–induced economic setbacks also likely exacerbated the psychological distress affecting broader aspects of well-being. Remote digital studies can provide information about pandemic's socioeconomic, mental, impact. COVIDsmart was a collaborative effort deploy complex research study...