Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Zheng JMIRMedInfo2017 5-2.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Tomich Sustain23 15-8.png|260px]]</div>
'''"[[Journal:Effective information extraction framework for heterogeneous clinical reports using online machine learning and controlled vocabularies|Effective information extraction framework for heterogeneous clinical reports using online machine learning and controlled vocabularies]]"'''
'''"[[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]"'''


Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedback for improving the extraction algorithm in real time.
Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the [[COVID-19]] [[pandemic]]. Mounting evidence and experience point to disturbing weaknesses in our food systems’ abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the “wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems ... ('''[[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Full article...]]''')<br />
 
''Recently featured'':
Our goal was to provide a generic [[information]] extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results.
{{flowlist |
 
* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]
A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedback to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. ('''[[Journal:Effective information extraction framework for heterogeneous clinical reports using online machine learning and controlled vocabularies|Full article...]]''')<br />
* [[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: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]]
''Recently featured'':  
}}
: ▪ [[Journal:Selecting a laboratory information management system for biorepositories in low- and middle-income countries: The H3Africa experience and lessons learned|Selecting a laboratory information management system for biorepositories in low- and middle-income countries: The H3Africa experience and lessons learned]]
: ▪ [[Journal:Baobab Laboratory Information Management System: Development of an open-source laboratory information management system for biobanking|Baobab Laboratory Information Management System: Development of an open-source laboratory information management system for biobanking]]
: ▪ [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|The FAIR Guiding Principles for scientific data management and stewardship]]

Revision as of 17:11, 22 April 2024

Fig1 Tomich Sustain23 15-8.png

"Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems"

Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the COVID-19 pandemic. Mounting evidence and experience point to disturbing weaknesses in our food systems’ abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the “wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems ... (Full article...)
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