Search

Your search keyword '"Schipper D"' showing total 19 results
Start Over Author "Schipper D" Language undetermined
19 results on '"Schipper D"'

1. Additional file 1 of Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Author

Dam, Tariq A., Roggeveen, Luca F., van Diggelen, Fuda, Fleuren, Lucas M., Jagesar, Ameet R., Otten, Martijn, de Vries, Heder J., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco A. A., Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Machado, Tomas, Herter, Willem E., de Grooth, Harm-Jan, Thoral, Patrick J., Girbes, Armand R. J., Hoogendoorn, Mark, and Elbers, Paul W. G.

Abstract

Additional file 1: Table S1. Parameter bounds. Table S2. Parameter categories. Table S3. Feature derivations. Table S4. Included features. Table S5. Hyper parameter optimization. Table S6. Feature Importance Metrics. Table S7. Patient characteristics. Table S8. ROC AUC score. Table S9. F1-score. Table S10. Sensitivity Analysis Minimal Prone Duration. Table S11. Sensitivity Analysis Cut-off Values. Table S12. Ranked Feature Importance. Table S13. Feature Importance Values. Figure S1. PaO2/FiO2 ratio difference in mmHg over time after starting prone position. Prone positions which continued for longer than 24 hours were filtered out.

Published
2022
Full Text
View/download PDF

2. Additional file 1 of Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Author

Fleuren, Lucas M., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Dam, Tariq A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, van der Meer, Nardo J. M., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert, Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Achterberg, Sefanja, Faber, Harald J., Karakus, A., Beukema, Menno, Entjes, Robert, de Jong, Paul, Houwert, Taco, Hovenkamp, Hidde, Noorduijn Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cinà, Giovanni, Beudel, Martijn, de Keizer, Nicolet F., Hoogendoorn, Mark, Girbes, Armand R. J., Herter, Willem E., Elbers, Paul W. G., and Thoral, Patrick J.

Abstract

Additional file 1: Figure S1. Patient selection. Figure S2. Selection of observations throughout the course of IMV. Figure S3. Nested cross-validation. Figure S4. Importance of the top 10 predictors for the prediction of ventilator free days, as well as the difference for predictors over time. Figure S5. SHAP plot ICU mortality (XGBoost). Figure S6. SHAP plot for ICU free days (XGBoost). Figure S7. SHAP plot for ventilator free days (XGBoost). Figure S8. PDPs. Table S1. Overview of all predictors used in the model with a definition where applicable. Table S2. Overall algorithm performance for each of the different outcomes. Table S3. Statistical results for a regression model per outcome. Table S4. Predictor correlations.

Published
2021
Full Text
View/download PDF

3. Additional file 3 of The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn-Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cina, Giovanni, Beudel, Martijn, Herter, Willem E., Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Abstract

Additional file 3: Table S1. Overview of derived clinical score.

Published
2021
Full Text
View/download PDF

4. Additional file 1 of Predictors for extubation failure in COVID-19 patients using a machine learning approach

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cin��, Giovanni, Kantorik, Adam, de Ruijter, Tom, Herter, Willem E., Beudel, Martijn, Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Abstract

Additional file 1. Table S1 Overview of machine learning studies that investigate extubation readiness. Table S2 Model performance. Figure S1 PD-plot for BMI

Published
2021
Full Text
View/download PDF

5. Additional file 2 of The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn-Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cina, Giovanni, Beudel, Martijn, Herter, Willem E., Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Subjects
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Data_FILES
Abstract

Additional file 2. Institutional review board documentation.

Published
2021
Full Text
View/download PDF

6. Technisch lego met een Duplo-interface

Author

Pimentel, A., Schipper, D., and System and Network Engineering (IVI, FNWI)

Abstract

Daedalus is een ontwerpflow op systeemniveau waarmee techneuten snel kunnen experimenteren met verschillende multiprocessorarchitecturen tijdens de vroege stadia van het ontwerptraject. Het is het resultaat van tien jaar onderzoek en ontwikkeling binnen de Progress-projecten Artemis en Artemisia. Projectleider Andy Pimentel en TUE-trainee Dorieke Schipper over de mogelijkheden van de Daedalussoftwareomgeving.

Published
2008

12. One year of COVID-19 in the Netherlands - a Dutch narrative

Author

Verbiest, D. P., Te Velde, L. F., Driel, E. M., Rijpstra, T., Elbers, P. H. J., Houwink, A. P. I., Georgieva, L., Verweij, E., Jong, R. M., Iersel, F. M., Koning, T. J. J., Rengers, E., Kusadasi, N., Erkamp, M. L., Den Berg, R., Jacobs, C. J. M. G., Epker, J. L., Rijkeboer, A. A., Bruin, M. T., Spronk, P., Draisma, A., Versluis, D. J., Den Berg, A. E., Vrolijk-De Mos, M., Lens, J. A., Pruijsten, R. V., Kieft, H., Rozendaal, J., Nooteboom, F., Boer, D. P., Janssen, I. T. A., Gulik, L., Koetsier, M. P., Silderhuis, V. M., Schnabel, R. M., Drogt, I., Ruijter, W., Bosman, R. J., Frenzel, T., Urlings-Strop, L. C., Dijkhuizen, A., Hene, I. Z., Meijer, A. R., Holtkamp, J. W. M., Postma, N., Bindels, A. J. G. H., Wesselink, R. M. J., Brinkman, S., Slobbe-Bijlsma, E. R., Voort, P. H. J., Eikemans, B. J. W., Mehagnoul-Schipper, D. J., Gommers, D., Lutisan, J. G., Hoeksema, M., Barnas, M. G. W., Festen-Spanjer, B., Lieshout, M., Gritters, N. C., Tellingen, M., Brunnekreef, G. B., Vandeputte, J., Dormans, T. P. J., Hoogendoorn, M. E., Graaff, M., Moolenaar, D., Reidinga, A. C., Spijkstra, J. J., Waal, R., Dongelmans, D. A., Dylan De Lange, Keizer, N. F., Surgery, Intensive care medicine, Elderly care medicine, Internal medicine, Cardio-thoracic surgery, Medical oncology, Psychiatry, APH - Mental Health, Pediatrics, Pediatric surgery, Cardiology, APH - Methodology, and ACS - Diabetes & metabolism

14. Analysis of the elasto-hydrodynamic lubrication in coated finite length line contacts

Author

Alakhramsing, S. S., Matthijn de Rooij, Schipper, D. J., and Drogen, M.

Subjects
Elastohydrodynamic lubrication, Coatings, Finite line contacts
Abstract

Due to the finite lengths of components, such as cam-roller followers pairs, rolling element bearings, gear teeth etc., high stresses are typically generated towards the extremities of the contact, often referred to as edge loading. Therefore axial surface profiling of the components is often utilized to minimize edge loading. Depending on the type of surface profiling, the pressure and film thickness distribution may deviate significantly from that predicted using the infinitely long line contact assumption, i.e. the film shapes near the extremities are very different from those at the central plane. In fact. the absolute minimum film thickness and maximum pressure, which are crucial design parameters, always occur near the position where axial surface profiling starts. This has also been proven both experimentally and theoretically by a number of researchers [1] [2] [3] [4]. Further, nowadays there is an increasing trend in the use of surface coatings in lubricated contacts. It has been observed from past studies (see for instance [5], [6] and [7] ) that the use of surface coatings can significantly enhance lubrication performance if the stiffness and thickness of the coating is designed properly. However, past studies concerning lubricated coated contacts, are mostly limited to infinite line contacts and circular contacts with and without coatings. Therefore, in this work a finite element method (FEM)-based finite line contact EHL model is developed, that includes the possibility of having a coating on both surfaces. As case study, the lubricated conjunction of an axially surface profiled coated roller on a coated plate is analyzed. The analysis is simplified by assuming that the two substrates and coatings are made of the same material, i.e. the coating and the bulk material of both surfaces have the Young’s moduli and Poisson’s ratios, although the coating can have different properties than the substrate. Here, the influence of operating conditions such as lubricant entrainment speed, load, lubricant properties, axial surface parameters nd coating material properties on the film thickness are analyzed.

18. Computational analysis of the lubricated-sliding contact with artificial slip boundary

Author

Tauviqirrahman, M., Rifky Ismail, Jamari, J., Schipper, D. J., and Faculty of Engineering Technology

Subjects
Physics::Fluid Dynamics, IR-90857, METIS-300300
Abstract

The hydrodynamic lubrication performance (load support, friction force, friction coefficient, and volume flow) at lubricated sliding contact is influenced by boundary condition. In the present paper, the effect of artificial slip boundary on the sliding surfaces is explored based on modified Reynolds equation using computational analysis. A design idea for an infinite sliding contact with the optimized artificial slip area is proposed. It is found that the generation of the artificial slip at the leading edge on the stationary surface of the contact gives the highest load support, which is helpful to improve the operation stability of the system. Optimization of the size of the slip area on the stationary surface can give many advanced properties compared with the classical no-slip surface, i.e., a reduction of friction and an increase of load support. It is also shown that if the artificial slip is employed on the moving surface, the system is in an unsteady state and no great importance exists in engineering applications.

19. The Low Biomass Yields of the Acetic Acid Bacterium Acetobacter pasteurianus Are Due to a Low Stoichiometry of Respiration-Coupled Proton Translocation

Author

Luttik, M. A., R.J.M. van Spanning, Schipper, D., Dijken, P. J., Pronk, J. T., Systems Bioinformatics, and AIMMS

Subjects
Journal Article, SDG 7 - Affordable and Clean Energy, SDG 6 - Clean Water and Sanitation
Abstract

Growth energetics of the acetic acid bacterium Acetobacter pasteurianus were studied with aerobic, ethanol-limited chemostat cultures. In these cultures, production of acetate was negligible. Carbon limitation and energy limitation were also evident from the observation that biomass concentrations in the cultures were proportional to the concentration of ethanol in the reservoir media. Nevertheless, low concentrations of a few organic metabolites (glycolate, citrate, and mannitol) were detected in culture supernatants. From a series of chemostat cultures grown at different dilution rates, the maintenance energy requirements for ethanol and oxygen were estimated at 4.1 mmol of ethanol (middot) g of biomass(sup-1) (middot) h(sup-1) and 11.7 mmol of O(inf2) (middot) g of biomass(sup-1) (middot) h(sup-1), respectively. When biomass yields were corrected for these maintenance requirements, the Y(infmax) values on ethanol and oxygen were 13.1 g of biomass (middot) mol of ethanol(sup-1) and 5.6 g of biomass (middot) mol of O(inf2)(sup-1), respectively. These biomass yields are very low in comparison with those of other microorganisms grown under comparable conditions. To investigate whether the low growth efficiency of A. pasteurianus might be due to a low gain of metabolic energy from respiratory dissimilation, (symbl)H(sup+)/O stoichiometries were estimated during acetate oxidation by cell suspensions. These experiments indicated an (symbl)H(sup+)/O stoichiometry for acetate oxidation of 1.9 (plusmn) 0.1 mol of H(sup+)/mol of O. Theoretical calculations of growth energetics showed that this low (symbl)H(sup+)/O ratio adequately explained the low biomass yield of A. pasteurianus in ethanol-limited cultures.

Catalog

Books, media, physical & digital resources
See catalog results