Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning

[1]  A. Nag.  Survival Analysis with Python , 2021 .

[2]  H. Weiner,et al.  Confirmed disability progression provides limited predictive information regarding future disease progression in multiple sclerosis , 2021, Multiple sclerosis journal - experimental, translational and clinical.

[3]  Peter Tiňo,et al.  A Survey on Neural Network Interpretability , 2020, IEEE Transactions on Emerging Topics in Computational Intelligence.

[4]  F. Barkhof,et al.  A randomized, placebo-controlled, phase 2 trial of laquinimod in primary progressive multiple sclerosis , 2020, Neurology.

[5]  D. Arnold,et al.  Contribution of Relapse-Independent Progression vs Relapse-Associated Worsening to Overall Confirmed Disability Accumulation in Typical Relapsing Multiple Sclerosis in a Pooled Analysis of 2 Randomized Clinical Trials , 2020, JAMA neurology.

[6]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[7]  Cameron Davidson-Pilon,et al.  lifelines: survival analysis in Python , 2019, J. Open Source Softw..

[8]  M. Sormani,et al.  Defining responders to therapies by a statistical modeling approach applied to randomized clinical trial data , 2019, BMC Medicine.

[9]  H. Weiner,et al.  Time between expanded disability status scale (EDSS) scores. , 2019, Multiple sclerosis and related disorders.

[10]  D. Arnold,et al.  Effect of natalizumab on disease progression in secondary progressive multiple sclerosis (ASCEND): a phase 3, randomised, double-blind, placebo-controlled trial with an open-label extension , 2018, The Lancet Neurology.

[11]  Ludwig Kappos,et al.  Siponimod versus placebo in secondary progressive multiple sclerosis (EXPAND): a double-blind, randomised, phase 3 study , 2018, The Lancet.

[12]  Ping Wang,et al.  Machine Learning for Survival Analysis , 2019, ACM Comput. Surv..

[13]  Chandan K. Reddy,et al.  Machine Learning for Survival Analysis: A Survey , 2017, ArXiv.

[14]  Pierre Gutierrez,et al.  Causal Inference and Uplift Modelling: A Review of the Literature , 2017, PAPIs.

[15]  Mihaela van der Schaar,et al.  Deep Counterfactual Networks with Propensity-Dropout , 2017, ArXiv.

[16]  Sören R. Künzel,et al.  Metalearners for estimating heterogeneous treatment effects using machine learning , 2017, Proceedings of the National Academy of Sciences.

[17]  Bernhard Hemmer,et al.  Ocrelizumab versus Placebo in Primary Progressive Multiple Sclerosis , 2017, The New England journal of medicine.

[18]  A. Traboulsee,et al.  Ocrelizumab versus Interferon Beta‐1a in Relapsing Multiple Sclerosis , 2017, The New England journal of medicine.

[19]  Karl R. Weiss,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[20]  Pierre Grammond,et al.  Defining reliable disability outcomes in multiple sclerosis. , 2015, Brain : a journal of neurology.

[21]  M. Sormani,et al.  Subgroups of multiple sclerosis patients with larger treatment benefits: a meta‐analysis of randomized trials , 2015, European journal of neurology.

[22]  Susan Athey,et al.  Recursive partitioning for heterogeneous causal effects , 2015, Proceedings of the National Academy of Sciences.

[23]  D. Arnold,et al.  A randomized placebo-controlled phase III trial of oral laquinimod for multiple sclerosis , 2014, Journal of Neurology.

[24]  Bernhard Hemmer,et al.  Siponimod for patients with relapsing-remitting multiple sclerosis (BOLD): an adaptive, dose-ranging, randomised, phase 2 study , 2013, The Lancet Neurology.

[25]  Sven F. Crone,et al.  Crogging (cross-validation aggregation) for forecasting — A novel algorithm of neural network ensembles on time series subsamples , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[26]  Lu Tian,et al.  Effectively Selecting a Target Population for a Future Comparative Study , 2013, Journal of the American Statistical Association.

[27]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[28]  R. Temple,et al.  Enrichment of Clinical Study Populations , 2010, Clinical pharmacology and therapeutics.

[29]  A. Ciapponi,et al.  Interferon Beta for primary progressive multiple sclerosis. , 2010, The Cochrane database of systematic reviews.

[30]  P. Calabresi,et al.  Rituximab in patients with primary progressive multiple sclerosis: Results of a randomized double‐blind placebo‐controlled multicenter trial , 2009, Annals of neurology.

[31]  Fred L. Drake,et al.  Python 3 Reference Manual , 2009 .

[32]  A. Thompson,et al.  Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. , 2008, Brain : a journal of neurology.

[33]  F. Schmidt.  Meta-Analysis , 2008 .

[34]  X. Montalban,et al.  Interferon beta in secondary progressive multiple sclerosis , 2007, Journal of Neurology.

[35]  P. Narayana,et al.  Glatiramer acetate in primary progressive multiple sclerosis: Results of a multinational, multicenter, double‐blind, placebo‐controlled trial , 2007, Annals of neurology.

[36]  À. Rovira,et al.  Baseline MRI predicts future attacks and disability in clinically isolated syndromes , 2006, Neurology.

[37]  R. Rudick,et al.  Significance of T2 lesions in multiple sclerosis: A 13‐year longitudinal study , 2006, Annals of neurology.

[38]  C. Polman,et al.  Infratentorial lesions predict long-term disability in patients with initial findings suggestive of multiple sclerosis. , 2004, Archives of neurology.

[39]  A. Thompson,et al.  Primary progressive multiple sclerosis: a 5-year clinical and MR study. , 2003, Brain : a journal of neurology.

[40]  N. Roberts,et al.  C erebral atrophy and disability in relapsing-remitting and secondary progressive multiple sclerosis over four years , 2003, Multiple sclerosis.

[41]  J H Simon,et al.  Eight-year follow-up study of brain atrophy in patients with MS , 2002, Neurology.

[42]  Ludwig Kappos,et al.  Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: a meta-analysis , 1999, The Lancet.

[43]  H. Baxter Williams,et al.  A Survey , 1992 .

[44]  P. Holland.  Statistics and Causal Inference , 1985 .

[45]  D. Rubin,et al.  Causal Inference for Statistics, Social, and Biomedical Sciences: Sensitivity Analysis and Bounds , 2015 .

[46]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[47]  Carlos D. Castillo,et al.  Feature Vector , 2009, Encyclopedia of Biometrics.

[48]  D. Altmann,et al.  Disability and T 2 MRI lesions: a 20 -year follow-up of patients with relapse onset of multiple sclerosis , 2008 .

[49]  A. R.,et al.  Review of literature , 1951, American Potato Journal.

[50]  THE MULTIPLE SCLEROSIS SOCIETY OF CANADA. , 1949, Canadian Medical Association journal.