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1. FHIR-Datenmodell zur Übermittlung von Tumordaten zwischen Krebsregistern und IQTIG

Author

Oeppert, Luise, Hartz, Tobias, Wehner, Kathrin, Schrader, Thomas, and Meier, Jens

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Infectious and parasitic diseases, RC109-216
Abstract

This project aimed at the implementation of an information model and a corresponding HL7 FHIR data model for the transmission of tumor data between cancer registries and the Institute for Quality Assurance and Transparency in Healthcare (IQTIG). The project is based on a concept study from IQTIG focusing on a future quality assurance measure of the treatment of patients with localized prostate cancer. In this study quality aspects have been developed, which means that different topics of interest have been adressed, but no specific quality indicators were developed. Based on the quality aspects possible data fields have been extracted which might be necessary for future quality indicators. Upon those information entities an information model was designed. Basn the information model a data model in HL7 FHIR has been developed and validated against a validation engine provided by the online platform Simplifier. This work shows the difference between an informaodel and a data model. The information model is crucial for establishing a common understanding between all stakeholders supporting interoperability. The data model is important for the underlying technology, but does not necessarily explain the overall process. Hence, informatnd data model complement each other. The theroretical work presented in this paper will be the basis for further projects. A following projell use the data model to transfer tumor data between the clinical cancer registry of Lower Saxony and the IQTIG.

Published
2021
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10. Diagnostic Quality Assessment for Low-Dimensional ECG Representations

Author

Kovács, Péter, Böck, Carl, Tschoellitsch, Thomas, Huemer, Mario, and Meier, Jens

Subjects
Electrical Engineering and Systems Science - Signal Processing, 62P10, 92C55, 41A30, 42C05, J.3, G.3, I.6.4
Abstract

There have been several attempts to quantify the diagnostic distortion caused by algorithms that perform low-dimensional electrocardiogram (ECG) representation. However, there is no universally accepted quantitative measure that allows the diagnostic distortion arising from denoising, compression, and ECG beat representation algorithms to be determined. Hence, the main objective of this work was to develop a framework to enable biomedical engineers to efficiently and reliably assess diagnostic distortion resulting from ECG processing algorithms. We propose a semiautomatic framework for quantifying the diagnostic resemblance between original and denoised/reconstructed ECGs. Evaluation of the ECG must be done manually, but is kept simple and does not require medical training. In a case study, we quantified the agreement between raw and reconstructed (denoised) ECG recordings by means of kappa-based statistical tests. The proposed methodology takes into account that the observers may agree by chance alone. Consequently, for the case study, our statistical analysis reports the "true", beyond-chance agreement in contrast to other, less robust measures, such as simple percent agreement calculations. Our framework allows efficient assessment of clinically important diagnostic distortion, a potential side effect of ECG (pre-)processing algorithms. Accurate quantification of a possible diagnostic loss is critical to any subsequent ECG signal analysis, for instance, the detection of ischemic ST episodes in long-term ECG recordings.

Published
2022
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13. ECG Beat Representation and Delineation by means of Variable Projection

Author

Böck, Carl, Kovács, Péter, Laguna, Pablo, Meier, Jens, and Huemer, Mario

Subjects
Electrical Engineering and Systems Science - Signal Processing, 65K10, 65D15, 92C55, G.1.6, G.1.2, G.1.10
Abstract

The electrocardiogram (ECG) follows a characteristic shape, which has led to the development of several mathematical models for extracting clinically important information. Our main objective is to resolve limitations of previous approaches, that means to simultaneously cope with various noise sources, perform exact beat segmentation, and to retain diagnostically important morphological information. Methods: We therefore propose a model that is based on Hermite and sigmoid functions combined with piecewise polynomial interpolation for exact segmentation and low-dimensional representation of individual ECG beat segments. Hermite and sigmoidal functions enable reliable extraction of important ECG waveform information while the piecewise polynomial interpolation captures noisy signal features like the BLW. For that we use variable projection, which allows the separation of linear and nonlinear morphological variations of the according ECG waveforms. The resulting ECG model simultaneously performs BLW cancellation, beat segmentation, and low-dimensional waveform representation. Results: We demonstrate its BLW denoising and segmentation performance in two experiments, using synthetic and real data (Physionet QT database). Compared to state-of-the-art algorithms, the experiments showed less diagnostic distortion in case of denoising and a more robust delineation for the P and T wave. Conclusion: This work suggests a novel concept for ECG beat representation, easily adaptable to other biomedical signals with similar shape characteristics, such as blood pressure and evoked potentials. Significance: Our method is able to capture linear and nonlinear wave shape changes. Therefore, it provides a novel methodology to understand the origin of morphological variations caused, for instance, by respiration, medication, and abnormalities., Comment: Codes, experiments, demonstrations are available at: https://codeocean.com/capsule/2038948/tree/v1

Published
2021
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23. Detection of Subtle ECG Changes Despite Superimposed Artifacts by Different Machine Learning Algorithms.

Author

Noitz, Matthias, Mörtl, Christoph, Böck, Carl, Mahringer, Christoph, Bodenhofer, Ulrich, Dünser, Martin W., and Meier, Jens

Subjects
ARTIFICIAL neural networks, RANDOM forest algorithms, INSPECTION & review, ELECTROCARDIOGRAPHY, PROOF of concept
Abstract

Analyzing electrocardiographic (ECG) signals is crucial for evaluating heart function and diagnosing cardiac pathology. Traditional methods for detecting ECG changes often rely on offline analysis or subjective visual inspection, which may overlook subtle variations, particularly in the case of artifacts. In this theoretical, proof-of-concept study, we investigated the potential of five different machine learning algorithms [random forests (RFs), gradient boosting methods (GBMs), deep neural networks (DNNs), an ensemble learning technique, as well as logistic regression] to detect subtle changes in the morphology of synthetically generated ECG beats despite artifacts. Following the generation of a synthetic ECG beat using the standardized McSharry algorithm, the baseline ECG signal was modified by changing the amplitude of different ECG components by 0.01–0.06 mV. In addition, a Gaussian jitter of 0.1–0.3 mV was overlaid to simulate artifacts. Five different machine learning algorithms were then applied to detect differences between the modified ECG beats. The highest discriminatory potency, as assessed by the discriminatory accuracy, was achieved by RFs and GBMs (accuracy of up to 1.0), whereas the least accurate results were obtained by logistic regression (accuracy approximately 10% less). In a second step, a feature importance algorithm (Boruta) was used to discriminate which signal parts were responsible for difference detection. For all comparisons, only signal components that had been modified in advance were used for discretion, demonstrating that the RF model focused on the appropriate signal elements. Our findings highlight the potential of RFs and GBMs as valuable tools for detecting subtle ECG changes despite artifacts, with implications for enhancing clinical diagnosis and monitoring. Further studies are needed to validate our findings with clinical data. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Influence of Filler Induced Cracks on the Statistical Lifetime of Rubber: A Review

Author

Meier, Jens, Robin, Stefan, Ludwig, Marvin, El Yaagoubi, Mohammed, Abe, Akihiro, Editorial Board Member, Albertsson, Ann-Christine, Editorial Board Member, Coates, Geoffrey W, Editorial Board Member, Genzer, Jan, Editorial Board Member, Kobayashi, Shiro, Editorial Board Member, Lee, Kwang-Sup, Editorial Board Member, Leibler, Ludwik, Editorial Board Member, Long, Timothy E., Editorial Board Member, Möller, Martin, Editorial Board Member, Okay, Oguz, Editorial Board Member, Percec, Virgil, Editorial Board Member, Tang, Ben Zhong, Editorial Board Member, Terentjev, Eugene M., Editorial Board Member, Theato, Patrick, Editorial Board Member, Voit, Brigitte, Editorial Board Member, Wiesner, Ulrich, Editorial Board Member, Zhang, Xi, Editorial Board Member, Heinrich, Gert, editor, Kipscholl, Reinhold, editor, and Stoček, Radek, editor

Published
2021
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25. Recommendations From the International Consensus Conference on Anemia Management in Surgical Patients (ICCAMS)

Author

Shander, Aryeh, Corwin, Howard L., Meier, Jens, Auerbach, Michael, Bisbe, Elvira, Blitz, Jeanna, Erhard, Jochen, Faraoni, David, Farmer, Shannon L., Frank, Steven M., Girelli, Domenico, Hall, Tiffany, Hardy, Jean-François, Hofmann, Axel, Lee, Cheuk-Kwong, Leung, Tsin W., Ozawa, Sherri, Sathar, Jameela, Spahn, Donat R., Torres, Rosalio, Warner, Matthew A., and Muñoz, Manuel

Published
2023
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26. Transfusion strategies in bleeding critically ill adults: a clinical practice guideline from the European Society of Intensive Care Medicine

Author

Vlaar, Alexander P. J., Dionne, Joanna C., de Bruin, Sanne, Wijnberge, Marije, Raasveld, S. Jorinde, van Baarle, Frank E. H. P., Antonelli, Massimo, Aubron, Cecile, Duranteau, Jacques, Juffermans, Nicole P., Meier, Jens, Murphy, Gavin J., Abbasciano, Riccardo, Müller, Marcella C. A., Lance, Marcus, Nielsen, Nathan D., Schöchl, Herbert, Hunt, Beverley J., Cecconi, Maurizio, and Oczkowski, Simon

Published
2021
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27. Recommendations from the International Consensus Conference on Anemia Management in Surgical Patients (ICCAMS)

Author

Shander, Aryeh, Corwin, Howard L., Meier, Jens, Auerbach, Michael, Bisbe, Elvira, Blitz, Jeanna, Erhard, Jochen, Faraoni, David, Farmer, Shannon, Frank, Steven M., Girelli, Domenico, Hall, Tiffany, Hardy, Jean-François, Hofmann, Axel, Lee, Cheuk-Kwong, Leung, Tsin Wah, Ozawa, Sherri, Sathar, Jameela, Spahn, Donat R., Torres, Rosalio, Warner, Matthew A., and Muñoz, Manuel

Published
2022
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29. Impact of the occurrence probability of carbon black agglomerates on the measured and predicted durability of filled elastomers

Author

Yaagoubi, Mohammed El and Meier, Jens

Subjects
Strains and stresses -- Analysis, Carbon-black -- Usage, Stress relaxation (Materials) -- Analysis, Elastomers -- Composition -- Mechanical properties, Stress relieving (Materials) -- Analysis, Engineering and manufacturing industries, Science and technology
Abstract

This work analyses the impact of the sample geometry on the stress or strain concentration and the occurrence of big agglomerates in the investigated test volume. Although the three investigated dumbbell test specimens were loaded at the same stress level, there were significant differences in measured lifetime. These differences in the lifetime are due to the occurrence of the large agglomerates in the tested volume. The probability of occurrence of large agglomerates increases with increase in the investigated volume. In the lifetime prediction by Monte Carlo method, this effect is realized by changing the quantity of the distributed agglomerates in the mesh of the simulated model. The prediction of the different sample geometries shows a very good agreement with the measurement points. The numerical simulations illustrate the stress increase for the three used lifetime test samples. KEYWORDS carbon black, crack propagation, durability, elastomers, lifetime prediction, Monte Carlo method, stress concentration, 1 | INTRODUCTION One of the main components of technical elastomer products is carbon black. Its most important function in elastomer applications is reinforcement. The reinforcement effects can be determined [...]

Published
2021
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30. Crack growth angle prediction of an internal crack under mixed mode load for unfilled elastomer using the strain energy density factor

Author

Yaagoubi, Mohammed El and Meier, Jens

Subjects
Materials -- Dynamic testing, Engineering and manufacturing industries, Science and technology
Abstract

This article investigates the prediction of the crack growth angle of an existing internal crack under mixed mode loading at the crack tip for an unfilled ethylene propylene diene terpolymer rubber (EPDM). For the realization of mixed mode loading, the cracks of the uniaxial loaded specimens were oriented with different angles to the loading direction. The energy density factor was used as a potential criterion for determining the crack growth angle. The determination of the strain energy density factor was carried out simulatively in Abaqus. The second-order Ogden model was used to describe the rubber-like material behavior. The relative local minimum of the strain energy density factor provides the possible growth angle. The experimental investigations show that the initial cracks grow orthogonally to the loading direction for the different crack orientation angles. For the crack orientation angle parallel to the load direction, the crack growth was observed because the strong stretching of the specimen caused strong necking in the crack region. The crack growth for the remaining crack orientation angles were induced due to shear loading at the crack tip. The predictive angle of different crack orientation angles shows very good accordance to the measured crack growth angles. KEYWORDS crack growth, crack growth angle, mixed mode, strain energy density factor, unfilled elastomers, 1 | INTRODUCTION Elastomer mixtures usually consist of polymer and other components such as carbon black or zinc oxide. The strong bonds between the polymer chains and the carbon black [...]

Published
2021
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31. A Linear Parameter Varying ARX Model for Describing Biomedical Signal Couplings

Author

Böck, Carl, Kostoglou, Kyriaki, Kovács, Péter, Huemer, Mario, Meier, Jens, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moreno-Díaz, Roberto, editor, Pichler, Franz, editor, and Quesada-Arencibia, Alexis, editor

Published
2020
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32. Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Author

Alfaro-Farias, Mario, Vizmanos-Lamotte, Gerardo, Tschoellitsch, Thomas, Meier, Jens, Aguirre-Bermeo, Hernán, Apolo, Janina, Martínez, Alberto, Jurkolow, Geoffrey, Delahaye, Gauthier, Novy, Emmanuel, Losser, Marie-Reine, Wengenmayer, Tobias, Rilinger, Jonathan, Staudacher, Dawid L., David, Sascha, Welte, Tobias, Stahl, Klaus, Pavlos”, “Agios, Aslanidis, Theodoros, Korsos, Anita, Babik, Barna, Nikandish, Reza, Rezoagli, Emanuele, Giacomini, Matteo, Nova, Alice, Fogagnolo, Alberto, Spadaro, Savino, Ceriani, Roberto, Murrone, Martina, Wu, Maddalena A., Cogliati, Chiara, Colombo, Riccardo, Catena, Emanuele, Turrini, Fabrizio, Simonini, Maria Sole, Fabbri, Silvia, Potalivo, Antonella, Facondini, Francesca, Gangitano, Gianfilippo, Perin, Tiziana, Grazia Bocci, Maria, Antonelli, Massimo, Gommers, Diederik, Rodríguez-García, Raquel, Gámez-Zapata, Jorge, Taboada-Fraga, Xiana, Castro, Pedro, Tellez, Adrian, Lander-Azcona, Arantxa, Escós-Orta, Jesús, Martín-Delgado, Maria C., Algaba-Calderon, Angela, Franch-Llasat, Diego, Roche-Campo, Ferran, Lozano-Gómez, Herminia, Zalba-Etayo, Begoña, Michot, Marc P., Klarer, Alexander, Ensner, Rolf, Schott, Peter, Urech, Severin, Zellweger, Nuria, Merki, Lukas, Lambert, Adriana, Laube, Marcus, Jeitziner, Marie M., Jenni-Moser, Beatrice, Wiegand, Jan, Yuen, Bernd, Lienhardt-Nobbe, Barbara, Westphalen, Andrea, Salomon, Petra, Drvaric, Iris, Hillgaertner, Frank, Sieber, Marianne, Dullenkopf, Alexander, Petersen, Lina, Chau, Ivan, Ksouri, Hatem, Sridharan, Govind Oliver, Cereghetti, Sara, Boroli, Filippo, Pugin, Jerome, Grazioli, Serge, Rimensberger, Peter C., Bürkle, Christian, Marrel, Julien, Brenni, Mirko, Fleisch, Isabelle, Lavanchy, Jerome, Perez, Marie-Helene, Ramelet, Anne-Sylvie, Weber, Anja Baltussen, Gerecke, Peter, Christ, Andreas, Ceruti, Samuele, Glotta, Andrea, Marquardt, Katharina, Shaikh, Karim, Hübner, Tobias, Neff, Thomas, Redecker, Hermann, Moret-Bochatay, Mallory, Bentrup, FriederikeMeyer zu, Studhalter, Michael, Stephan, Michael, Brem, Jan, Gehring, Nadine, Selz, Daniela, Naon, Didier, Kleger, Gian-Reto, Pietsch, Urs, Filipovic, Miodrag, Ristic, Anette, Sepulcri, Michael, Heise, Antje, Franchitti Laurent, Marilene, Laurent, Jean-Christophe, Wendel Garcia, Pedro D., Schuepbach, Reto, Heuberger, Dorothea, Bühler, Philipp, Brugger, Silvio, Fodor, Patricia, Locher, Pascal, Camen, Giovanni, Gaspert, Tomislav, Jovic, Marija, Haberthuer, Christoph, Lussman, Roger F., Colak, Elif, Montomoli, Jonathan, Romeo, Luca, Moccia, Sara, Bernardini, Michele, Migliorelli, Lucia, Berardini, Daniele, Donati, Abele, Carsetti, Andrea, Bocci, Maria Grazia, Wendel Garcia, Pedro David, Fumeaux, Thierry, Guerci, Philippe, Schüpbach, Reto Andreas, Ince, Can, Frontoni, Emanuele, and Hilty, Matthias Peter

Published
2021
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38. Continuous Detection of Stimulus Brightness Differences Using Visual Evoked Potentials in Healthy Volunteers with Closed Eyes.

Author

Kalb, Stephan, Böck, Carl, Bolz, Matthias, Schlömmer, Christine, Kudumija, Lucija, Dünser, Martin W., and Meier, Jens

Subjects
VISUAL evoked potentials, MACHINE learning, SIGNAL processing, VOLUNTEERS, VOLUNTEER service, BRAIN stimulation, TRANSCRANIAL alternating current stimulation
Abstract

Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is visionary in the understanding of visual signal processing and will facilitate the deepening of our knowledge concerning anesthetic research. Methods: X-gradient boosting models were used to classify the cortical response to visual stimulation (no light vs. light stimulations and two lights with different brightnesses). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), and training across all but one participant with prediction performed in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulations (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96 ± 0.05, accuracyindividual: 0.94 ± 0.05; AUCROConeout: 0.98 ± 0.04; accuracyoneout: 0.96 ± 0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between light stimulations with different brightnesses (AUCROCall: 0.97; accuracyall: 0.91; AUCROCindividual: 0.98 ± 0.04, accuracyindividual: 0.96 ± 0.04; AUCROConeout: 0.96 ± 0.05; accuracyoneout: 0.93 ± 0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or light stimulations using visual evoked potentials in awake female and male volunteers with eyes closed. Our findings may open new possibilities for the use of visual evoked potentials in the clinical and intraoperative setting. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Prognostic factors associated with mortality risk and disease progression in 639 critically ill patients with COVID-19 in Europe: Initial report of the international RISC-19-ICU prospective observational cohort

Author

Alfaro Farias, Mario, Margarit, Antoni, Vizmanos-Lamotte, Gerardo, Tschoellitsch, Thomas, Meier, Jens, Cardona, Francesco S., Skola, Josef, Horakova, Lenka, Aguirre-Bermeo, Hernan, Apolo, Janina, Novy, Emmanuel, Losser, Marie-Reine, Jurkolow, Geoffrey, Delahaye, Gauthier, David, Sascha, Welte, Tobias, Wengenmayer, Tobias, Staudacher, Dawid L., Aslanidis, Theodoros, Babik, Barna, Korsos, Anita, Gal, Janos, Csaba, Hermann, Donati, Abele, Carsetti, Andrea, Turrini, Fabrizio, Simonini, Maria Sole, Ceriani, Roberto, Murrone, Martina, Rezoagli, Emanuele, Vitale, Giovanni, Fogagnolo, Alberto, Spadaro, Savino, Wu, Maddalena Alessandra, Cogliati, Chiara, Colombo, Riccardo, Catena, Emanuele, Facondini, Francesca, Potalivo, Antonella, Gangitano, Gianfilippo, Perin, Tiziana, Bocci, Maria Grazia, Antonelli, Massimo, Gommers, Diederik, Ince, Can, Mayor-Vázquez, Eric, Cruz, Maria, Delgado, Martin, Garcia, Raquel Rodriguez, Gamez Zapata, Jorge, Zalba-Etayo, Begoña, Lozano-Gomez, Herminia, Castro, Pedro, Tellez, Adrian, Jacas, Adriana, Muñoz, Guido, Andrea, Rut, Ortiz, Jose, Quintana, Eduard, Rovira, Irene, Reverter, Enric, Fernandez, Javier, Ferrer, Miquel, Badia, Joan R., Lander Azcona, Arantxa, Orta, Jesus Escos, Bühler, Philipp, Brugger, Silvio, Hofmaenner, Daniel, Unseld, Simone, Ruschitzka, Frank, Moret-Bochatay, Mallory, Yuen, Bernd, Hillermann, Thomas, Ksouri, Hatem, Sridharan, Govind Oliver, Ristic, Anette, Sepulcri, Michael, Filipovic, Miodrag, Pietsch, Urs, Salomon, Petra, Drvaric, Iris, Schott, Peter, Urech, Severin, Lambert, Adriana, Merki, Lukas, Laube, Marcus, Hillgaertner, Frank, Sieber, Marianne, Dullenkopf, Alexander, Petersen, Lina, Grazioli, Serge, Rimensberger, Peter C., Fleisch, Isabelle, Lavanchy, Jerome, Marquardt, Katharina, Shaikh, Karim, Redecker, Hermann, Stephan, Michael, Brem, Jan, Rogdo, Bjarte, Birkenmaier, Andre, Meyer zu Bentrup, Friederike, Fodor, Patricia, Locher, Pascal, Camen, Giovanni, Siegemund, Martin, Zellweger, Nuria, Jeitziner, Marie-Madlen, Jenni-Moser, Beatrice, Bürkle, Christian, Kleger, Gian-Reto, Franchitti Laurent, Marilene, Laurent, Jean-Christophe, Gaspert, Tomislav, Jovic, Marija, Studhalter, Michael, Haberthuer, Christoph, Lussman, Roger F., Selz, Daniela, Naon, Didier, Mauri, Romano, Ceruti, Samuele, Marrel, Julien, Brenni, Mirko, Ensner, Rolf, Gehring, Nadine, Heise, Antje, Huebner, Tobias, Neff, Thomas A., Cereghetti, Sara, Boroli, Filippo, Pugin, Jerome, Marczin, Nandor, Wong, Joyce, Wendel Garcia, Pedro David, Fumeaux, Thierry, Guerci, Philippe, Heuberger, Dorothea Monika, Montomoli, Jonathan, Roche-Campo, Ferran, Schuepbach, Reto Andreas, and Hilty, Matthias Peter

Published
2020
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45. Transfusion strategies in non-bleeding critically ill adults: a clinical practice guideline from the European Society of Intensive Care Medicine

Author

Vlaar, Alexander P., Oczkowski, Simon, de Bruin, Sanne, Wijnberge, Marije, Antonelli, Massimo, Aubron, Cecile, Aries, Philippe, Duranteau, Jacques, Juffermans, Nicole P., Meier, Jens, Murphy, Gavin J., Abbasciano, Riccardo, Muller, Marcella, Shah, Akshay, Perner, Anders, Rygaard, Sofie, Walsh, Timothy S., Guyatt, Gordon, Dionne, J. C., and Cecconi, Maurizio

Published
2020
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46. Global Decision Making for Wavelet Based ECG Segmentation

Author

Böck, Carl, Lunglmayr, Michael, Mahringer, Christoph, Mörtl, Christoph, Meier, Jens, Huemer, Mario, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Moreno-Díaz, Roberto, editor, Pichler, Franz, editor, and Quesada-Arencibia, Alexis, editor

Published
2018
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50. Care of the critically ill begins in the emergency medicine setting

Author

Al-Hadad, Mo, Arafat, Raed, Baker, Tim, Balik, Martin, Behringer, Wilhelm, Brown, Ruth, Carenzo, Luca, Connolly, Jim, Dankl, Daniel, Dodt, Christoph, Dünser, Martin W., Exadaktylos, Aristomenis, Gauss, Tobias, Gavrilovic, Srdjan, Hachimi-Idrissi, Said, Haenggi, Matthias, Herkner, Harald, Joannidis, Michael, Khoury, Abdo, Klinglmair, Michaela, Leach, Robert, Leone, Marc, Lockey, David, Meier, Jens, Noitz, Matthias, Petrino, Roberta, Petros, Sirak, Plaisance, Patrick, Preller, Jacobus, Riesgo, Luis Garcia-Castrillo, Schell, Carl Otto, Šeblová, Jana, Sitzwohl, Christian, Skjaerbaek, Christian Baaner, Skrifvars, Markus, Sunde, Kjetil, Mahečić, Tina Tomić, Trimmel, Helmut, Valentin, Andreas, and Wenzel, Volker

Published
2024
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