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

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(Updated article of the week text)
(Updated article of the week text)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Ayaz Healthcare23 11-12.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Ishizuki SciTechAdvMatMeth2023 3-1.jpeg|240px]]</div>
'''"[[Journal:Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data|Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data]]"'''
'''"[[Journal:Autonomous experimental systems in materials science|Autonomous experimental systems in materials science]]"'''


In this study, we discuss our contribution to building a [[Data analysis|data analytic]] framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named [[Fast Healthcare Interoperability Resources]] (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several [[workflow]]s for patient clinical data used in two [[hospital information system]]s (HISs), namely patient registration systems (PRSs) and [[laboratory information system]]s (LIS). These workflows exploit various FHIR [[application programming interface]]s (API) to facilitate patient-centered and cohort-based interactive analyses. We developed a FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection ... ('''[[Journal:Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data|Full article...]]''')<br />
The emergence of [[Laboratory automation|autonomous experimental systems]] (AESs) integrating [[machine learning]] (ML) and robots is ushering in a paradigm shift in [[materials science]]. Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention. A current direction focuses on discovering unexpected materials and theories with unconventional [[research]] approaches. This article reviews the latest achievements and discusses the impact of AESs, which will fundamentally change the way we understand research. Moreover, as AESs continue to develop, the need to think about the role of human researchers becomes more pressing ... ('''[[Journal:Autonomous experimental systems in materials science|Full article...]]''')<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data|Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data]]
* [[Journal:Laboratory automation, informatics, and artificial intelligence: Current and future perspectives in clinical microbiology|Laboratory automation, informatics, and artificial intelligence: Current and future perspectives in clinical microbiology]]
* [[Journal:Laboratory automation, informatics, and artificial intelligence: Current and future perspectives in clinical microbiology|Laboratory automation, informatics, and artificial intelligence: Current and future perspectives in clinical microbiology]]
* [[Journal:Registered data-centered lab management system based on data ownership security architecture|Registered data-centered lab management system based on data ownership security architecture]]
* [[Journal:Registered data-centered lab management system based on data ownership security architecture|Registered data-centered lab management system based on data ownership security architecture]]
* [[Journal:FAIR Health Informatics: A health informatics framework for verifiable and explainable data analysis|FAIR Health Informatics: A health informatics framework for verifiable and explainable data analysis]]
}}
}}

Revision as of 17:24, 27 November 2023

Fig1 Ishizuki SciTechAdvMatMeth2023 3-1.jpeg

"Autonomous experimental systems in materials science"

The emergence of autonomous experimental systems (AESs) integrating machine learning (ML) and robots is ushering in a paradigm shift in materials science. Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention. A current direction focuses on discovering unexpected materials and theories with unconventional research approaches. This article reviews the latest achievements and discusses the impact of AESs, which will fundamentally change the way we understand research. Moreover, as AESs continue to develop, the need to think about the role of human researchers becomes more pressing ... (Full article...)
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