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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Teixeira FutureInternet2018 10-8.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:SCADA system testbed for cybersecurity research using machine learning approach|SCADA system testbed for cybersecurity research using machine learning approach]]"'''
'''"[[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]]"'''


This paper presents the development of a [[supervisory control and data acquisition]] (SCADA) system testbed used for [[cybersecurity]] research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|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...)

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