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A '''[[federally qualified health center]]''' ('''FQHC''') is a reimbursement designation from the [[Centers for Medicare and Medicaid Services]] (CMS) of the [[United States Department of Health and Human Services]] (HHS). This designation is significant for several health programs funded under Section 330 of the Public Health Service Act, as part of the Health Center Consolidation Act. The FQHC program is designed "to enhance the provision of primary care services in underserved urban and rural communities."
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


FQHCs are community-based organizations that provide comprehensive primary and preventive care, including health, oral, and mental health services to persons of all ages, regardless of their ability to pay or health insurance status. Thus, they are a critical component of the health care safety net. As of 2011 over 1,100 FQHCs operate approximately 6,000 sites throughout the United States and territories, serving an estimated 20 million patients. That number is expected to go up to 40 million people by 2015 thanks to extra grant funding to the program. FQHCs may also be referred to as community/migrant health centers (C/MHC), community health centers (CHC), and 330 funded clinics. ('''[[Federally qualified health center|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'': [[Home health agency]], [[ISO 9000]], [[Health Level 7]]
{{flowlist |
* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
}}

Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
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