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 Hodhod IntJofOnlineBiomedEng2019 15-3.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Wang BMCMedInfoDecMak2019 19-1.png|240px]]</div>
'''"[[Journal:CyberMaster: An expert system to guide the development of cybersecurity curricula|CyberMaster: An expert system to guide the development of cybersecurity curricula]]"'''
'''"[[Journal:Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory|Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory]]"'''


The growing number of reported cyberattacks poses a difficult challenge to individuals, governments, and organizations. Adequate protection of [[information]] systems urgently requires a [[cybersecurity]]-educated workforce trained using a curriculum that covers the essential skills required for different cybersecurity work roles. The goal of the CyberMaster [[expert system]] is to assist inexperienced instructors with cybersecurity course design. It is an intelligent system that uses visual feedback to guide the user through the design process. Initial test executions show the promise of such a system in addressing the enormous shortage of cybersecurity experts currently available for designing courses and training programs. ('''[[Journal:CyberMaster: An expert system to guide the development of cybersecurity curricula|Full article...]]''')<br />
n autoverification system for coagulation consists of a series of rules that allows normal data to be released without manual verification. With new advances in [[medical informatics]], the [[laboratory information system]] (LIS) has growing potential for the use of autoverification, allowing rapid and accurate verification of [[clinical laboratory]] tests. The purpose of the study is to develop and evaluate a LIS-based autoverification system for validation and efficiency.
 
Autoverification decision rules—including quality control, analytical error flag, critical value, limited range check, delta check, and logical check rules, as well as patient’s historical information—were integrated into the LIS. Autoverification limit ranges was constructed based on 5% and 95% percentiles. The four most commonly used coagulation assays—prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FBG)—were followed by the autoverification protocols. ('''[[Journal:Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory|Full article...]]''')<br />
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Revision as of 15:52, 11 November 2019

Fig1 Wang BMCMedInfoDecMak2019 19-1.png

"Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory"

n autoverification system for coagulation consists of a series of rules that allows normal data to be released without manual verification. With new advances in medical informatics, the laboratory information system (LIS) has growing potential for the use of autoverification, allowing rapid and accurate verification of clinical laboratory tests. The purpose of the study is to develop and evaluate a LIS-based autoverification system for validation and efficiency.

Autoverification decision rules—including quality control, analytical error flag, critical value, limited range check, delta check, and logical check rules, as well as patient’s historical information—were integrated into the LIS. Autoverification limit ranges was constructed based on 5% and 95% percentiles. The four most commonly used coagulation assays—prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FBG)—were followed by the autoverification protocols. (Full article...)

Recently featured:

CyberMaster: An expert system to guide the development of cybersecurity curricula
Costs of mandatory cannabis testing in California
An integrated data analytics platform