AIMC Topic: Public Health Surveillance
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Identifying real time surveillance indicators to estimate COVID-19 hospital admissions in Colorado during and after the public health emergency.
Scientific reports
Jul 1, 2025
Questions remain about how best to focus surveillance efforts for COVID-19 and other emerging respiratory diseases. We used an archive of COVID-19 data in Colorado from October 2020 to March 2024 to reconstruct seven real-time surveillance indicators...
Precision in Prevention and Health Surveillance: How Artificial Intelligence May Improve the Time of Identification of Health Concerns through Social Media Content Analysis.
Yearbook of medical informatics
Apr 8, 2025
OBJECTIVE: To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance "precision in prevention and health surveillance" (2024 Yearbook topic). The focus is on leveraging advanced ...
Mandatory surveillance of bacteremia conducted by automated monitoring.
Frontiers in public health
Dec 16, 2024
Except for a few countries, comprehensive all-cause surveillance for bacteremia is not part of mandatory routine public health surveillance. We argue that time has come to include automated surveillance for bacteremia in the national surveillance sys...
Innovation in public health surveillance for social distancing during the COVID-19 pandemic: A deep learning and object detection based novel approach.
PloS one
Sep 9, 2024
The Corona Virus Disease (COVID-19) has a huge impact on all of humanity, and people's disregard for COVID-19 regulations has sped up the disease's spread. Our study uses a state-of-the-art object detection model like YOLOv4 (You Only Look Once, vers...
Addressing bias in artificial intelligence for public health surveillance.
Journal of medical ethics
Feb 20, 2024
Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes ...
Risk factor assessments of temporomandibular disorders via machine learning.
Scientific reports
Oct 5, 2021
This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieve...
Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.
Scientific reports
Jun 10, 2021
Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal caregivers due to the motor and cognitive decline caused by the disease. This study aims to identify ca...
Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.
PloS one
May 19, 2021
Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza a...
Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.
Scandinavian journal of trauma, resuscitation and emergency medicine
Mar 31, 2021
OBJECTIVES: During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdo...
Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 3, 2021
Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it i...
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