Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n...
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| Published in | Journal of clinical epidemiology Vol. 122; no. 2; pp. 95 - 107 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.06.2020
Elsevier Limited Elsevier Elsevier USA |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0895-4356 1878-5921 1878-5921 |
| DOI | 10.1016/j.jclinepi.2020.03.005 |
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| Abstract | We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.
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.
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.
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. |
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| AbstractList | 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 We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. 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. 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. 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. ObjectiveWe 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 SettingWe 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.ResultsIn 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.ConclusionML 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. 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 (LR), lasso, 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 GCS<13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (AUC) was quantified. RESULTS: In the IMPACT-II database, 3,332/11,022(30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale below 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 AUC was 0.82 for mortality and 0.77 for unfavorable outcome in CENTER-TBI. CONCLUSION: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe TBI. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. 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. 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. AbstractObjectiveWe 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 SettingWe performed logistic (LR), lasso, 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 GCS<13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (AUC) was quantified. ResultsIn the IMPACT-II database, 3,332/11,022(30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale below 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 AUC was 0.82 for mortality and 0.77 for unfavorable outcome in CENTER-TBI. ConclusionML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe TBI. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.OBJECTIVEWe aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.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.STUDY DESIGN AND SETTINGWe 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.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.RESULTSIn 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.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.CONCLUSIONML 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. |
| Author | Tibboel, Dick Esser, Patrick Cabeleira, Manuel Takala, Riikka Jacobs, Bram Rosenthal, Guy Blaabjerg, Morten Sakowitz, Oliver Andreassen, Lasse Bullinger, Monika Martin Fabricius, Erzsébet Ezer Laureys, Steven Giga, Lelde Majdan, Marek Lingsma, Hester F. Benali, Habib Peul, Wilco Valeinis, Egils Smielewski, Peter Vámos, Zoltán Ghuysen, Alexandre Kolias, Angelos G. Perlbarg, Vincent Thomas, Matt Zeiler, Frederik A. Rosand, Jonathan Vajkoczy, Peter Buki, Andras Vallance, Shirley Lightfoot, Roger Feigin, Kelly Foks, Valery L. Timmers, Marjolein Schwendenwein, Elisabeth Maréchal, Hugues Huijben, Jilske Kondziella, Daniel Maas, Andrew I.R. Beretta, Luigi Coburn, Mark Haitsma, Iain Oresic, Matej Ragauskas, Arminas Trapani, Tony Antoni, Anna Velt, Kimberley Sewalt, Charlie Helbok, Raimund Vanhaudenhuyse, Audrey Steyerberg, Ewout W. Gratz, Johannes Synnot, Anneliese Cameron, Peter Karan, Mladen Palotie, Aarno Wilson, Lindsay Czosnyka, Marek Gomez, Pedro A. Gravesteijn, Benjamin Y. Zoerle, Tommaso Lecky, Fiona Tamosuitis, Tomas Tolias, Christos Golubovic, |
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| ContentType | Journal Article |
| Contributor | Esser, Patrick Cabeleira, Manuel Ji-Yao Jiang, Mike Jarrett Jacobs, Bram Blaabjerg, Morten Gravesteijn, Benjamin Andreassen, Lasse Bullinger, Monika Martin Fabricius, Erzsébet Ezer Andelic, Nada Laureys, Steven Giga, Lelde Benali, Habib Kolias, Angelos G Guy-Loup Dulière, Jens Dreier Ghuysen, Alexandre Calappi, Emiliana Anke, Audny Buki, Andras Ercole, Ari Huijben, Jilske Kondziella, Daniel Kovács, Noémi Azouvi, Philippe Brooker, Joanne Beretta, Luigi Coburn, Mark Haitsma, Iain Frisvold, Shirin Dawes, Helen Haagsma, Juanita A Antoni, Anna Đilvesi, Đula Amrein, Krisztina Helbok, Raimund Gratz, Johannes Cameron, Peter Karan, Mladen Czosnyka, Marek Coles, Jonathan Koskinen, Lars-Owe Lecky, Fiona Beauvais, Romuald Caccioppola, Alessio Azzolini, Maria Luisa Dahyot-Fizelier, Claire Golubovic, Jagoš Grossi, Francesca Brazinova, Alexandra Čović, Amra Furmanov, Alex Carbonara, Marco Gao, Guoyi Dixit, Abhishek Gantner, Dashiell Barzó, Pál Horton, Lindsay Donoghue, Emma Belli, Antonio Brorsson, Camilla Hutchinson, Peter J Czeiter, Endre Depreitere, Bart Cnos CHU de Liège-Centre du Cerveau² - ULiège Oresic, Matej |
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| Copyright | 2020 The Authors The Authors Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved. 2020. The Authors info:eu-repo/semantics/openAccess Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1016/j.jclinepi.2020.03.005 |
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| Issue | 2 |
| Keywords | Data science Prognosis Traumatic brain injury Cohort study Machine learning Prediction NN LR Gradient boosting machine Glasgow coma scale SVM Support vector machine Neural network Random Forest Logistic regression GBM RF GCS TBI ML |
| Language | English |
| License | This is an open access article under the CC BY license. Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 other-oa |
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| Snippet | We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.
We... AbstractObjectiveWe aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic... ObjectiveWe aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain... We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain... Objective - We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain... Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain... OBJECTIVE: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain... |
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| SubjectTerms | Adult Algorithms Artificial neural networks Brain Brain Injuries, Traumatic - therapy Calibration Cohort analysis Cohort study Coma Confidence intervals Data science Datasets Decision Making, Computer-Assisted Epidemiology Female Glasgow Coma Scale Head injuries Human health and pathology Humans Internal Medicine Laboratories Learning algorithms Life Sciences Logistic Models Machine Learning Male Medical disciplines: 700 Medical prognosis Medical technology: 620 Medisinsk teknologi: 620 Medisinske Fag: 700 Middle Aged Models, Statistical Mortality Neural networks Neurosciences & behavior Neurosciences & comportement Prediction Prediction models Prognosis Regression analysis Regression models Sciences sociales & comportementales, psychologie Social & behavioral sciences, psychology Support vector machines Technology: 500 Teknologi: 500 Traumatic brain injury Variables VDP |
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| Title | Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury |
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