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.
2020In Journal of Clinical Epidemiology, 122, p. 95-107
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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
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 ;  Université de Liège - ULiège > Département de Psychologie > Département de Psychologie
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;  V.L.
Frisvold, S.
Furmanov, A.
Gagliardo, P.
Galanaud, D.
Gantner, D.
Gao, G.
George, Pradeep
Ghuysen, Alexandre ;  Université de Liège - ULiège > Département des sciences de la santé publique > Simulation médicale en situation critique
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  ;  Université de Liège - ULiège > Giga Consciousness-Coma Science Group
Lecky, F.
Lefering, R.
Legrand, Victor ;  Université de Liège - ULiège > Département des sciences cliniques > Département des sciences cliniques
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 ;  Université de Liège - ULiège > GIGA CRC In vivo Imaging
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.
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
More authors (233 more) Less
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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