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An '''[[end-stage renal disease facility]]''' ('''ESRD facility''', '''dialysis facility''', or '''dialysis center''') is a medical facility that operates to assist people with irreversible loss of kidney function (stage five), requiring a regular course of dialysis or a kidney transplant to survive. The facility may operate independently, as part of a [[hospital]]-based unit, or as a self-care unit that furnishes only self-dialysis services.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The U.S. [[Centers for Medicare and Medicaid Services]] (CMS) describes four types of ESRD facilities, including the renal transplantation center, for ESRD transplant patients; the renal dialysis center, a hospital unit for ESRD dialysis patients; a renal dialysis facility, a direct or stand-alone dialysis unit for ESRD patients; and a self-dialysis unit attached to one of the previous three, providing self-dialysis services. Patients undergoing dialysis at these facilities require two important documentation steps: the patient assessment and the patient plan of care. U.S. Federal regulation requires a comprehensive 13-point assessment, including current health status, laboratory profile, and nutritional status. ('''[[End-stage renal disease facility|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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{{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]]
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* [[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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