Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study
The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (...
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| Veröffentlicht in: | NPJ digital medicine Jg. 3; H. 1; S. 135 - 8 |
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| Sprache: | Englisch |
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16.10.2020
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| Abstract | The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in
Expanded Disability Status Scale
(
EDSS
) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (
SVM, Logistic Regression,
and
Random Forest
) and three ensemble learning approaches (
XGBoost, LightGBM
, and a Meta-learner
L
). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS,
Pyramidal Function
, and
Ambulatory Index
were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course. |
|---|---|
| AbstractList | The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women's Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients' clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A "threshold" was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women's Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients' clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A "threshold" was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course. Abstract The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course. The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women's Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in ( ) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients' clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms ( and ) and three ensemble learning approaches ( , and a Meta-learner ). A "threshold" was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, , and were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course. The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale ( EDSS ) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms ( SVM, Logistic Regression, and Random Forest ) and three ensemble learning approaches ( XGBoost, LightGBM , and a Meta-learner L ). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function , and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course. The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course. |
| ArticleNumber | 135 |
| Author | Zhao, Yijun Bakshi, Rohit Lokhande, Hrishikesh Polgar-Turcsanyi, Mariann Anderson, Mark Chitnis, Tanuja Weiner, Howard L. Bove, Riley Henry, Roland Wang, Tong Cree, Bruce |
| Author_xml | – sequence: 1 givenname: Yijun surname: Zhao fullname: Zhao, Yijun organization: Department of Computer and Information Science, Fordham University – sequence: 2 givenname: Tong surname: Wang fullname: Wang, Tong organization: Department of Computer and Information Science, Fordham University – sequence: 3 givenname: Riley surname: Bove fullname: Bove, Riley organization: University of California, SUMMIT Consortium, SUMMIT Consortium – sequence: 4 givenname: Bruce surname: Cree fullname: Cree, Bruce organization: University of California, SUMMIT Consortium, SUMMIT Consortium – sequence: 5 givenname: Roland surname: Henry fullname: Henry, Roland organization: University of California, SUMMIT Consortium, SUMMIT Consortium – sequence: 6 givenname: Hrishikesh surname: Lokhande fullname: Lokhande, Hrishikesh organization: Brigham Multiple Sclerosis Center, Ann Romney Center, Brigham and Women’s Hospital, Harvard Medical School – sequence: 7 givenname: Mariann orcidid: 0000-0002-1244-1802 surname: Polgar-Turcsanyi fullname: Polgar-Turcsanyi, Mariann organization: SUMMIT Consortium, SUMMIT Consortium, Brigham Multiple Sclerosis Center, Ann Romney Center, Brigham and Women’s Hospital, Harvard Medical School – sequence: 8 givenname: Mark surname: Anderson fullname: Anderson, Mark organization: SUMMIT Consortium, SUMMIT Consortium, Brigham Multiple Sclerosis Center, Ann Romney Center, Brigham and Women’s Hospital, Harvard Medical School – sequence: 9 givenname: Rohit orcidid: 0000-0001-8601-5534 surname: Bakshi fullname: Bakshi, Rohit organization: SUMMIT Consortium, SUMMIT Consortium, Brigham Multiple Sclerosis Center, Ann Romney Center, Brigham and Women’s Hospital, Harvard Medical School – sequence: 10 givenname: Howard L. surname: Weiner fullname: Weiner, Howard L. organization: SUMMIT Consortium, SUMMIT Consortium, Brigham Multiple Sclerosis Center, Ann Romney Center, Brigham and Women’s Hospital, Harvard Medical School – sequence: 11 givenname: Tanuja orcidid: 0000-0002-9897-4422 surname: Chitnis fullname: Chitnis, Tanuja email: tchitnis@rics.bwh.harvard.edu organization: SUMMIT Consortium, SUMMIT Consortium, Brigham Multiple Sclerosis Center, Ann Romney Center, Brigham and Women’s Hospital, Harvard Medical School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33083570$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
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| Keywords | Multiple sclerosis |
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In Proc. of the 31st Conference on Neural Information Processing Systems (NIPS, Long Beach, CA, 2017). – reference: WeinshenkerBGThe natural history of multiple sclerosis: a geographically based study: 2 predictive value of the early clinical courseBrain19891121419142810.1093/brain/112.6.1419 – reference: KurtzkeJFRating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS)Neurology198333144414441:STN:280:DyaL2c%2FktValsQ%3D%3D10.1212/WNL.33.11.1444 – reference: WeinshenkerBGThe natural history of multiple sclerosis: a geographically based study: I. Clinical course and disabilityBrain198911213314610.1093/brain/112.1.133 – reference: GauthierSAGlanzBIMandelMWeinerHLA model for the comprehensive investigation of a chronic autoimmune disease: the multiple sclerosis CLIMB studyAutoimmun. Rev.200655325361:CAS:528:DC%2BD28Xht12qtL3E10.1016/j.autrev.2006.02.012 – reference: Hosmer, D. W. Jr, Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression, Vol. 398 (Wiley, Hoboken, 2013). – reference: KapposLEffect of early versus delayed interferon beta-1b treatment on disability after a first clinical event suggestive of multiple sclerosis: a 3-year follow-up analysis of the BENEFIT studyLancet20073703893971:CAS:528:DC%2BD2sXosFKktLc%3D10.1016/S0140-6736(07)61194-5 – reference: BreimanLRandom forestsMach. Learn.20014553210.1023/A:1010933404324 – reference: WolpertDHStacked generalizationNeural Netw.1992524125910.1016/S0893-6080(05)80023-1 – reference: Chen, T. & Guestrin, C. Xgboost: A scalable tree boosting system. In Proc. of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794 (KDD, New York, NY, 2016). – reference: Amato, M. & Ponziani, G. A prospective study on the prognosis of multiple sclerosis. Neurol. Sci.21, S831–S838 (2000). – reference: Kasturi, S. N. XGBOOST vs LightGBM: Which Algorithm Wins the Race!!! https://towardsdatascience.com/lightgbm-vs-xgboost-which-algorithm-win-the-race-1ff7dd4917d (2019). – reference: CortesCVapnikVSupport-vector networksMach. Learn.199520273297 – volume: 24 start-page: 1485 year: 2018 ident: 338_CR15 publication-title: Mult. Scler. doi: 10.1177/1352458517726657 – volume: 65 start-page: 1044 year: 2008 ident: 338_CR8 publication-title: Arch. Neurol. doi: 10.1001/archneurol.65.8.noc80042 – volume: 1 start-page: 81 year: 1986 ident: 338_CR14 publication-title: Mach. Learn. – ident: 338_CR22 doi: 10.1145/2939672.2939785 – volume: 129 start-page: 595 year: 2006 ident: 338_CR2 publication-title: Brain doi: 10.1093/brain/awh714 – volume: 20 start-page: 273 year: 1995 ident: 338_CR18 publication-title: Mach. 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Med. doi: 10.1056/NEJMoa067597 – volume: 5 start-page: 532 year: 2006 ident: 338_CR16 publication-title: Autoimmun. Rev. doi: 10.1016/j.autrev.2006.02.012 – volume: 14 start-page: 771 year: 1999 ident: 338_CR24 publication-title: J.-Jpn. Soc. Artif. Intell. – volume: 112 start-page: 133 year: 1989 ident: 338_CR6 publication-title: Brain doi: 10.1093/brain/112.1.133 – ident: 338_CR5 doi: 10.1007/s100720070021 – volume: 33 start-page: 1444 year: 1983 ident: 338_CR10 publication-title: Neurology doi: 10.1212/WNL.33.11.1444 – volume: 112 start-page: 1419 year: 1989 ident: 338_CR7 publication-title: Brain doi: 10.1093/brain/112.6.1419 – reference: 33299133 - NPJ Digit Med. 2020 Nov 20;3(1):155 |
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| SubjectTerms | 692/617/375/1411/1666 692/617/375/1666 Algorithms Biomedicine Biotechnology Datasets Digital technology Health informatics Machine learning Medicine Medicine & Public Health Multiple sclerosis |
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| Title | Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study |
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