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Population Health

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Creative Approaches for Assessing Long-term Outcomes in Children.

Pediatrics
Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To as...

Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19.

Medical science monitor : international medical journal of experimental and clinical research
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pneumonia. However, COVID-19 is now recognized to involve multiple organ systems with varying severity ...

Deep learning for prediction of population health costs.

BMC medical informatics and decision making
BACKGROUND: Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing.

Ethical Implications of Artificial Intelligence in Population Health and the Public's Role in Its Governance: Perspectives From a Citizen and Expert Panel.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) systems are widely used in the health care sector. Mainly applied for individualized care, AI is increasingly aimed at population health. This raises important ethical considerations but also calls for respons...

[Digital environment and population health].

Revue medicale suisse
Health and risk of disease are determined by exposure to the physical, socio-economic, and political environment and to this has been added exposure to the digital environment. Our increasingly digital lives have major implications for people's healt...

Advocating for population health: The role of public health practitioners in the age of artificial intelligence.

Canadian journal of public health = Revue canadienne de sante publique
Over the past decade, artificial intelligence (AI) has begun to transform Canadian organizations, driven by the promise of improved efficiency, better decision-making, and enhanced client experience. While AI holds great opportunities, there are also...

Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health.

The Milbank quarterly
Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in health care and regulate its application in legal and ethical ways. Significant progress has been made ...

Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

BMC public health
BACKGROUND: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population healt...

Artificial intelligence, recessionary pressures and population health.

Bulletin of the World Health Organization
Economic and labour policies have a considerable influence on health and well-being through direct financial impacts, and by shaping social and physical environments. Strong economies are important for public health investment and employment, yet the...