Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Gravesteijn, Benjamin Y.; Nieboer, Daan; Ercole, Ari; Lingsma, Hester F.; Nelson, David W.; van Calster, Ben; Steyerberg, E. W.; Åkerlund, Cecilia; Amrein, Krisztina; Andelic, Nada; Andreassen, Lasse; Anke, Audny; Antoni, Anna; Audibert, Gérard; Azouvi, Philippe; Azzolini, Maria Luisa; Bartels, Ronald; Barzó, Pál; Beauvais, Romuald; Beer, Ronny; Bellander, Bo Michael; Belli, Antonio; Benali, Habib; Berardino, Maurizio; Beretta, Luigi; Blaabjerg, M.; Bragge, P.; Brazinova, A.; Brinck, V.; Brooker, J.; Brorsson, C.; Buki, A.; Bullinger, M.; Cabeleira, M.; Caccioppola, A.; Calappi, E.; Calvi, M. R.; Cameron, P.; Lozano, G. C.; Carbonara, M.; Chevallard, G.; Chieregato, A.; Citerio, G.; Cnossen, M.; Coburn, M.; Coles, J.; Cooper, D. J.; Correia, M.; Čović, A.; Curry, N.; Czeiter, E.; Czosnyka, M.; Dahyot-Fizelier, C.; Dawes, H.; De Keyser, Véronique; Degos, V.; Della Corte, F.; Boogert, H. D.; Depreitere, B.; Đilvesi, Đ.; Dixit, A.; Donoghue, E.; Guy-Loup Dulière, J. D.; Esser, P.; Martin Fabricius, E. E.; Feigin, Kelly Foks; Frisvold, S.; Furmanov, A.; Gagliardo, P.; Galanaud, D.; Gantner, D.; Gao, G.; George, Pradeep; Ghuysen, Alexandre; Giga, L.; Glocker, B.; Golubovic, J.; Gomez, P. A.; Gratz, J.; Gravesteijn, B.; Grossi, F.; Gruen, R. L.; Gupta, D.; Haagsma, J. A.; Haitsma, I.; Helbok, R.; Helseth, E.; Horton, L.; Huijben, J.; Hutchinson, P. J.; Jacobs, B.; Jankowski, S.; Ji-yao Jiang, M. J.; Jones, K.; Karan, M.; Kolias, A. G.; Kompanje, E.; Kondziella, D.; Koraropoulos, E.; Koskinen, L.-O.; Kovács, N.; Lagares, A.; Lanyon, L.; Laureys, Steven; Lecky, F.; Lefering, R.; Legrand, Victor; Lejeune, Aurélie; Levi, L.; Lightfoot, R.; Lingsma, H.; Maas, A. I. R.; Castaño-León, A. M.; Maegele, M.; Majdan, M.; Manara, A.; Manley, G.; Martino, C.; Maréchal, H.; Mattern, J.; McMahon, C.; Melegh, B.; Menon, D.; Menovsky, T.; Mulazzi, D.; Muraleedharan, V.; Murray, L.; Nair, N.; Negru, A.; Newcombe, V.; Noirhomme, Quentin; Nyirádi, J.; Olubukola, O.; Oresic, M.; Ortolano, F.; Palotie, A.; Parizel, P. M.; Payen, J.-F.; Perera, N.; Perlbarg, V.; Persona, P.; Peul, W.; Piippo-Karjalainen, A.; Pirinen, M.; Ples, H.; Polinder, S.; Pomposo, I.; Posti, J. P.; Puybasset, L.; Radoi, A.; Ragauskas, A.; Raj, R.; Rambadagalla, M.; Real, R.; Rhodes, J.; Richardson, S.; Richter, S.; Ripatti, S.; Rocka, S.; Roe, C.; Roise, O.; Rosand, J.; Rosenfeld, J. V.; Rosenlund, C.; Rosenthal, G.; Rossaint, R.; Rossi, S.; Rueckert, D.; Rusnák, M.; Sahuquillo, J.; Sakowitz, O.; Sanchez-Porras, R.; Sandor, J.; Schäfer, N.; Schmidt, S.; Schoechl, H.; Schoonman, G.; Schou, R. F.; Schwendenwein, E.; Sewalt, C.; Skandsen, T.; Smielewski, P.; Sorinola, A.; Stamatakis, E.; Stanworth, S.; Kowark, A.; Stevens, R.; Stewart, W.; Stocchetti, N.; Sundström, N.; Synnot, A.; Takala, R.; Tamás, V.; Tamosuitis, T.; Taylor, M. S.; Ao, B. T.; Tenovuo, O.; Theadom, A.; Thomas, M.; Tibboel, D.; Timmers, M.; Tolias, C.; Trapani, T.; Tudora, C. M.; Vajkoczy, P.; Vallance, S.; Valeinis, E.; Vámos, Z.; Van der Steen, G.; van der Naalt, J.; van Dijck, J. T. J. M.; van Essen, T. A.; Van Hecke, W.; van Heugten, C.; Van Praag, D.; Vyvere, T. V.; VANHAUDENHUYSE, Audrey; van Wijk, R. P. J.; Vargiolu, A.; Vega, E.; Velt, K.; Verheyden, J.; Vespa, P. M.; Vik, A.; Vilcinis, R.; Volovici, V.; von Steinbüchel, N.; Voormolen, D.; Vulekovic, P.; Wang, K. K. W.; Wiegers, E.; Williams, G.; Wilson, L.; Winzeck, S.; Wolf, S.; Yang, Z.; Ylén, P.; Younsi, A.; Zeiler, F. A.; Zelinkova, V.; Ziverte, A.; Zoerle, T.; CENTER-TBI collaborators

doi:10.1016/j.jclinepi.2020.03.005

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Article (Scientific journals)

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Gravesteijn, Benjamin Y.; Nieboer, Daan; Ercole, Ari et al.

2020 • In Journal of Clinical Epidemiology, 122, p. 95-107

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/249323

DOI
10.1016/j.jclinepi.2020.03.005

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32201256

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Keywords :

Cohort study; Data science; Machine learning; Prediction; Prognosis; Traumatic brain injury; Article; Glasgow coma scale

Abstract :

[en] Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. © 2020 The Authors

Research center :

CHU de Liège-Centre du Cerveau² - ULiège

Disciplines :

Neurosciences & behavior

Author, co-author :

Gravesteijn, Benjamin Y.; Departments of Public Health, Erasmus MC – University Medical Centre Rotterdam, Postbus 2040, Rotterdam, CA 3000, Netherlands

Nieboer, Daan; Departments of Public Health, Erasmus MC – University Medical Centre Rotterdam, Rotterdam, Netherlands

Ercole, Ari; Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom

Lingsma, Hester F.; Departments of Public Health, Erasmus MC – University Medical Centre Rotterdam, Rotterdam, Netherlands

Nelson, David W.; Department of Physiology and Pharmacology, Section of Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden

van Calster, Ben; Department of Development and Regeneration, KU Leuven, Belgium, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands

Steyerberg, E. W.; Departments of Public Health, Erasmus MC – University Medical Centre Rotterdam, Rotterdam, Netherlands, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands

Åkerlund, Cecilia

Amrein, Krisztina

Andelic, Nada

More authors (233 more)

Language :

English

Title :

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Publication date :

2020

Journal title :

Journal of Clinical Epidemiology

ISSN :

0895-4356

eISSN :

1878-5921

Publisher :

Elsevier USA

Volume :

122

Pages :

95-107

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 07 July 2020

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