Machine learning for the prediction of preoxygenation technique in trauma

Background Preoxygenation can be achieved best by non-invasive ventilation techniques (NIV).Objective With the help of machine learning, the decision-making process against or in favour of NIV for preoxygenation in severely injured preclinical patients shall be evaluated.Methods A registry-based, retrospective analysis in preclinical adult trauma patients in south-western Germany between 2018 to 2020 was conducted. Attributes considered were the initial vital signs, Glasgow Coma Scale, airway devices, administered medication, description of difficult airway, emergency interventions, shock index, age and pre emergency status. A decision tree model (REPTree) and two Bayesian network (BN) were created, one with all and the other with the attributes occurring in the decision tree. Results 992 datasets with 333 cases of NIV (33%) were identified. Main splitting points in the decision tree model were the attributes rhonchus and bronchial spasm, videolaryngoscopy, respiratory rate, heart rate, age, oxygen saturation and head injury. The area under the receiver operating characteristics was between 0.97 (original BN; 95% CI, 0.96-0.97) and 0.93 (REPTree, 95% CI, 0.92-0.93). For the prediction, the precision-recall area was 0.96 (BN, 95% CI, 0.96-0.97) and 0.88 (REPTree, 95% CI, 0.87-0.89) and for exclusion 0.96 (BN, 95% CI, 0.96-0.97) and 0.94 (REPTree, 65% CI, 0.93-0.94). The simplified BN performed equally to the original BN.Conclusion The presented models demonstrated a feasibility for modeling decision making as well as an excellent performance. An expended model should contain internal and neurological patients as well as the effectiveness of the chosen method and could therefore support emergency medical crews.Files:Supplement Bayesian Network max 3 nodes XML BIF.xml•XML data file with all nodes and probabilities of the final Bayesian network with a maximum of 3 parental nodes that can be implemented in WEKASupplement Simplified Bayesian Network max 3 nodes XML BIF.xml•XML data file with all nodes and probabilities of the simplified Bayesian network with a maximum of 3 parental nodes that can be implemented in WEK

Machine learning for the prediction of preoxygenation technique in trauma

Background Preoxygenation can be achieved best by non-invasive ventilation techniques (NIV).Objective With the help of machine learning, the decision-making process against or in favour of NIV for preoxygenation in severely injured preclinical patients shall be evaluated.Methods A registry-based, retrospective analysis in preclinical adult trauma patients in south-western Germany between 2018 to 2020 was conducted. Attributes considered were the initial vital signs, Glasgow Coma Scale, airway devices, administered medication, description of difficult airway, emergency interventions, shock index, age and pre emergency status. A decision tree model (REPTree) and two Bayesian network (BN) were created, one with all and the other with the attributes occurring in the decision tree. Results 992 datasets with 333 cases of NIV (33%) were identified. Main splitting points in the decision tree model were the attributes rhonchus and bronchial spasm, videolaryngoscopy, respiratory rate, heart rate, age, oxygen saturation and head injury. The area under the receiver operating characteristics was between 0.97 (original BN; 95% CI, 0.96-0.97) and 0.93 (REPTree, 95% CI, 0.92-0.93). For the prediction, the precision-recall area was 0.96 (BN, 95% CI, 0.96-0.97) and 0.88 (REPTree, 95% CI, 0.87-0.89) and for exclusion 0.96 (BN, 95% CI, 0.96-0.97) and 0.94 (REPTree, 65% CI, 0.93-0.94). The simplified BN performed equally to the original BN.Conclusion The presented models demonstrated a feasibility for modeling decision making as well as an excellent performance. An expended model should contain internal and neurological patients as well as the effectiveness of the chosen method and could therefore support emergency medical crews.Files:Supplement Bayesian Network max 3 nodes XML BIF.xml•XML data file with all nodes and probabilities of the final Bayesian network with a maximum of 3 parental nodes that can be implemented in WEKASupplement Simplified Bayesian Network max 3 nodes XML BIF.xml•XML data file with all nodes and probabilities of the simplified Bayesian network with a maximum of 3 parental nodes that can be implemented in WEKATHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

High level nuclear waste glass corrosion in synthetic clay pore solution and retention of actinides in secondary phases

The corrosion of the simulated high level waste glass GP WAK1 in synthetic clay pore solution was studied in batch-type experiments at 323 and 363 K with special focus on the effect of high carbonate concentration in solution. The corrosion rate after 130 days was < 10(-4) g m(-2) d(-1) - no significant effect of the carbonate was identified. During glass corrosion, crystalline secondary phases (powellite, barite, calcite, anhydrite and clay-like Mg(Ca,Fe)-silicates) were formed. To obtain a molecular level picture of radionuclide speciation within the alteration layer, spectroscopic methods have been applied including grazing incidence X-ray absorption spectroscopy (XAS) to study the structural changes in the coordination of uranyl upon alteration layer formation. The number of equatorial oxygen atoms increases from 4 in the bulk glass to 5 in the alteration layer. Furthermore, reduced coordination symmetry was found. Hectorite, a frequently observed secondary clay mineral within the glass alteration layer, was synthesized in the presence of trivalent f-elements (e.g. Eu) and structurally characterized using time-resolved laser fluorescence spectroscopy. Structural incorporation into the octahedral layer is indicated. (c) 2008 Elsevier B.V. All rights reserved

Verglasung konventioneller, schwermetallhaltiger Abfaelle aus Muellverbrennungsanlagen

Bei der Muellverbrennung entstehen Flugaschen, die Schwermetalle in loeslicher Form enthalten. Um diese Rueckstaende deponieren oder gar wiederverwerten zu koennen, ist eine Nachbehandlung der Flugaschen erforderlich, z.B. durch Abtrennung der Schwermetalle oder durch Verglasung. Verschiedene Verfahren zur Konditionierung schwermetallhaltiger Abfaelle befinden sich in der Entwicklung oder Erprobung, die sich unter Sinter- und Schmelzverfahren zusammenfassen lassen. Allen Verfahren gemeinsam ist die Erzeugung eines glasigen Rueckstandes, der wiederverwertet werden soll. Von allen Verfahren ist das SOLUR-Verfahren das einzige ''echte'' Verglasungsverfahren, bei dem in einem Glasschmelzofen schwermetallhaltige Abfaelle, insbesondere Flugaschen, direkt oder unter Zuschlag von Sand, Altglas oder Phonolith in ein homogenes, porenarmes Glasprodukt ueberfuehrt werden. Fuer die Wiederverwertung muss das Glasprodukt chemisch so bestaendig sein, dass eine nennenswerte Auslaugung der Schwermetalle ausgeschlossen werden kann. Aus verfahrenstechnischen Gruenden ergeben sich besondere Anforderungen an den Viskositaetverlauf, den spezifischen elektrischen Widerstand und an das Kristallisationsverhalten der Schmelze. (orig.)Combustion of municipal waste yields fly ashes. The ashes contain hazardous heavy metals in a soluble form. Recycling or disposal of such residues requires separation of the hazardous metals or vitrification of the ashes. For recycling the glass product must be chemically durable enough to prevent significant leaching of the heavy metals. Various treatment processes are under development or testing. For the time being, only one ''real'' vitrification process for fly ashes has been described in the literature: the SOLUR glass melting process, developed by the companies Sorg and Lurgi. Various wastes such as fly ashes, slags and sludges are fed into a direct-heated ceramic melter and additives like sand or bottle glass can be added to adjust the glass composition. The toxic elements are immobilized in the structure of the glass, which is produced in form of chunks, pellets or granulate. The vitrification process requires knowledge of the viscosity, the specific electrical resistance and the crystallization properties of the melt. (orig.)SIGLEAvailable from TIB Hannover: ZA 5141(5270) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman