Difference between revisions of "Main Page/Featured article of the week/2023"

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The Appalachian population is distinct, not just culturally and geographically but also in its healthcare needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized [[data warehouse]] to transform healthcare data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center, along with its initial uses ... ('''[[Journal:Fueling clinical and translational research in Appalachia: Informatics platform approach|Full article...]]''')<br />
The Appalachian population is distinct, not just culturally and geographically but also in its healthcare needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized [[data warehouse]] to transform healthcare data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center, along with its initial uses ... ('''[[Journal:Fueling clinical and translational research in Appalachia: Informatics platform approach|Full article...]]''')<br />
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 02–08:</h2>
|<br /> //--><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 02–08:</h2>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Sachdeva Information22 13-2.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Giray Sustain21 13-13.png|240px]]</div>
'''"[[Journal:Using knowledge graph structures for semantic interoperability in electronic health records data exchanges|Using knowledge graph structures for semantic interoperability in electronic health records data exchanges]]"'''
'''"[[Journal:Design of a data management reference architecture for sustainable agriculture|Design of a data management reference architecture for sustainable agriculture]]"'''


[[Data sharing|Information sharing]] across medical institutions is restricted to [[information]] exchange between specific partners. The lifelong [[electronic health record]] (EHR) structure and content require standardization efforts. Existing standards such as [[openEHR]], [[Health Level 7]] (HL7), and ISO/EN 13606 aim to achieve data independence along with semantic interoperability. This study aims to discover knowledge representation to achieve semantic health data exchange. openEHR and ISO/EN 13606 use archetype-based technology for semantic interoperability. The HL7 Clinical Document Architecture is on its way to adopting this through HL7 templates. Archetypes are the basis for knowledge-based systems, as these are means to define clinical knowledge. The paper examines a set of formalisms for the suitability of describing, representing, and reasoning about archetypes. Each of the information exchange technologies—such as XML, Web Ontology Language (OWL), Object Constraint Language (OCL), and Knowledge Interchange Format (KIF)—is evaluated as a part of the knowledge representation experiment. These examine the representation of archetypes as described by Archetype Definition Language (ADL) ... ('''[[Journal:Using knowledge graph structures for semantic interoperability in electronic health records data exchanges|Full article...]]''')<br /> //-->
Effective and efficient [[Information management|data management]] is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as [[machine learning]] and deep learning. Due to the complexity of data management operations, a data management [[reference architecture]] is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture ... ('''[[Journal:Design of a data management reference architecture for sustainable agriculture|Full article...]]''')<br />
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Revision as of 17:01, 9 January 2023

Featured article of the week archive - 2023

Welcome to the LIMSwiki 2023 archive for the Featured Article of the Week.

Featured article of the week: January 02–08:

Fig3 Giray Sustain21 13-13.png

"Design of a data management reference architecture for sustainable agriculture"

Effective and efficient data management is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture ... (Full article...)