{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:17:54Z","timestamp":1742987874161,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031723834"},{"type":"electronic","value":"9783031723841"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72384-1_38","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:02:53Z","timestamp":1727866973000},"page":"400-410","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Probabilistic Temporal Prediction of\u00a0Continuous Disease Trajectories and\u00a0Treatment Effects Using Neural SDEs"],"prefix":"10.1007","author":[{"given":"Joshua","family":"Durso-Finley","sequence":"first","affiliation":[]},{"given":"Berardino","family":"Barile","sequence":"additional","affiliation":[]},{"given":"Jean-Pierre","family":"Falet","sequence":"additional","affiliation":[]},{"given":"Douglas L.","family":"Arnold","sequence":"additional","affiliation":[]},{"given":"Nick","family":"Pawlowski","sequence":"additional","affiliation":[]},{"given":"Tal","family":"Arbel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","volume":"76","author":"M Abdar","year":"2021","unstructured":"Abdar, M., et\u00a0al.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion 76, 243-297 (2021)","journal-title":"Inf. Fusion"},{"issue":"21","key":"38_CR2","doi-asserted-by":"publisher","first-page":"3589","DOI":"10.1002\/sim.2672","volume":"25","author":"D Ashby","year":"2006","unstructured":"Ashby, D.: Bayesian statistics in medicine: a 25 year review. Statistics in medicine 25(21), 3589\u20133631 (2006)","journal-title":"Stat. Med."},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Barrow, D.K., Crone, S.F.: Crogging (cross-validation aggregation) for forecasting - a novel algorithm of neural network ensembles on time series subsamples. In: The 2013 International Joint Conference on Neural Networks (IJCNN) (2013)","DOI":"10.1109\/IJCNN.2013.6706740"},{"issue":"1","key":"38_CR4","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1177\/1756285614564152","volume":"8","author":"R Bomprezzi","year":"2015","unstructured":"Bomprezzi, R.: Dimethyl fumarate in the treatment of relapsing-remitting multiple sclerosis: an overview. Ther. Adv. Neurol. Disord. 8(1), 20\u201330 (2015)","journal-title":"Ther. Adv. Neurol. Disord."},{"issue":"4","key":"38_CR5","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1016\/j.neuroimage.2007.10.026","volume":"39","author":"RG Boyes","year":"2008","unstructured":"Boyes, R.G., et\u00a0al.: Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. Neuroimage 39(4), 1752-1762 (2008)","journal-title":"Neuroimage"},{"issue":"7","key":"38_CR6","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/S1474-4422(14)70068-7","volume":"13","author":"PA Calabresi","year":"2014","unstructured":"Calabresi, P.A., et\u00a0al.: Pegylated interferon beta-1a for relapsing-remitting multiple sclerosis (ADVANCE): a randomised, phase 3, double-blind study. Lancet Neurol. 13(7), 657\u2013665 (2014)","journal-title":"Lancet Neurol."},{"key":"38_CR7","unstructured":"Chen, T., et\u00a0al.: Neural Ordinary Differential Equations. CoRR (2018)"},{"issue":"2","key":"38_CR8","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1097\/00004728-199403000-00005","volume":"18","author":"DL Collins","year":"1994","unstructured":"Collins, D.L., et\u00a0al.: Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18(2), 192-205 (1994)","journal-title":"J. Comput. Assist Tomogr."},{"issue":"08","key":"38_CR9","first-page":"1271","volume":"11","author":"L Collins","year":"2011","unstructured":"Collins, L., et\u00a0al.: Animal: validation and applications of nonlinear registration-based segmentation. IJPRAI 11(08), 1271\u20131294 (2011)","journal-title":"IJPRAI"},{"key":"38_CR10","unstructured":"De\u00a0Brouwer, E., et\u00a0al.: GRU-ODE-bayes: continuous modeling of sporadically-observed time series. In: Advances in Neural Information Processing Systems (2019)"},{"key":"38_CR11","unstructured":"Durso-Finley, J., et\u00a0al.: Personalized prediction of future lesion activity and treatment effect in multiple sclerosis from baseline MRI. In: MICCAI (2022)"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Durso-Finley, J., et\u00a0al.: Improving image-based precision medicine with uncertainty-aware causal models. In: MICCAI (2023)","DOI":"10.1007\/978-3-031-43904-9_46"},{"issue":"1","key":"38_CR13","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1038\/s41467-021-22265-2","volume":"12","author":"A Eshaghi","year":"2021","unstructured":"Eshaghi, A., et\u00a0al.: Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nature Communications 12(1), 2078 (2021)","journal-title":"Nat. Commun."},{"key":"38_CR14","doi-asserted-by":"publisher","first-page":"102096","DOI":"10.1016\/j.media.2021.102096","volume":"72","author":"C Fang","year":"2021","unstructured":"Fang, C., et\u00a0al.: Deep learning for predicting COVID-19 malignant progression. Medical Image Analysis 72, 102096 (2021)","journal-title":"Med. Image Anal."},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Gold, R., et\u00a0al.: Safety and efficacy of delayed-release dimethyl fumarate in patients with relapsing-remitting multiple sclerosis: 9 years\u2019 follow-up of DEFINE, CONFIRM, and ENDORSE. her Adv Neurol Disord (2020)","DOI":"10.1177\/1756286420915005"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Hauser, S.L., et\u00a0al.: Ocrelizumab versus Interferon Beta-1a in Relapsing Multiple Sclerosis. New England J. Med. 376(3), 221\u2013234 (2017)","DOI":"10.1056\/NEJMoa1601277"},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"He, K., et\u00a0al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Imbens, G., Rubin, D.: Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press, Cambridge (2015)","DOI":"10.1017\/CBO9781139025751"},{"key":"38_CR19","unstructured":"Kapoor, R., et\u00a0al.: 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. The Lancet Neurology (2018)"},{"key":"38_CR20","unstructured":"Kidger, P., et\u00a0al.: Neural SDEs as infinite-dimensional GANs. In: International Conference on Machine Learning (2021)"},{"key":"38_CR21","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et\u00a0al.: A survey on deep learning in medical image analysis. Medical image analysis 42, 60-88 (2017)","journal-title":"Med. Image Anal."},{"key":"38_CR22","doi-asserted-by":"crossref","unstructured":"Lu, J., et\u00a0al.: Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens. iScience 24, 102804 (2021)","DOI":"10.1016\/j.isci.2021.102804"},{"issue":"6","key":"38_CR23","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1002\/1531-8249(200006)47:6<707::AID-ANA3>3.0.CO;2-Q","volume":"47","author":"C Lucchinetti","year":"2000","unstructured":"Lucchinetti, C., et\u00a0al.: Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Annals of neurology 47(6), 707\u2013717 (2000)","journal-title":"Ann. Neurol."},{"issue":"1","key":"38_CR24","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/jmri.22003","volume":"31","author":"JV Manj\u00f3n","year":"2010","unstructured":"Manj\u00f3n, J.V., et\u00a0al.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging 31(1), 192\u2013203 (2010)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"3","key":"38_CR25","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1056\/NEJMoa1606468","volume":"376","author":"X Montalban","year":"2017","unstructured":"Montalban, X., et\u00a0al.: Ocrelizumab versus placebo in primary progressive multiple sclerosis. N Engl. J. Med. 376(3), 209\u2013220 (2017)","journal-title":"N Engl. J. Med."},{"issue":"9","key":"38_CR26","first-page":"e45412","volume":"15","author":"Y Naji","year":"2023","unstructured":"Naji, Y., et\u00a0al.: Artificial intelligence and multiple sclerosis: up-to-date review. Cureus 15(9), e45412 (2023)","journal-title":"Cureus"},{"key":"38_CR27","unstructured":"Norcliffe, A., et\u00a0al.: Benchmarking continuous time models for predicting multiple sclerosis progression. Trans. Mach. Learn. Res. (2023)"},{"issue":"1","key":"38_CR28","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1111\/j.1467-9531.2010.01228.x","volume":"40","author":"J Pearl","year":"2010","unstructured":"Pearl, J.: The foundations of causal inference. Soc. Methodol. 40(1), 75-149 (2010)","journal-title":"Soc. Methodol."},{"key":"38_CR29","doi-asserted-by":"publisher","unstructured":"Pontillo, G., et\u00a0al.: Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach. Eur. Radiol. 32, 5382\u20135391 (2022). https:\/\/doi.org\/10.1007\/s00330-022-08610-z","DOI":"10.1007\/s00330-022-08610-z"},{"key":"38_CR30","unstructured":"Qian, Z., et\u00a0al.: Integrating expert ODEs into neural ODEs: pharmacology and disease progression. In: Advances in Neural Information Processing Systems (2021)"},{"key":"38_CR31","doi-asserted-by":"crossref","unstructured":"Ren, J., et\u00a0al.: Balanced MSE for imbalanced visual regression. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00777"},{"issue":"11","key":"38_CR32","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1001\/archneurol.2010.150","volume":"67","author":"RA Rudick","year":"2010","unstructured":"Rudick, R.A., et\u00a0al.: Disability progression in a clinical trial of relapsing-remitting multiple sclerosis: eight-year follow-up. Archives of Neurology 67(11), 1329\u20131335 (2010)","journal-title":"Arch. Neurol."},{"issue":"8","key":"38_CR33","doi-asserted-by":"publisher","first-page":"220638","DOI":"10.1098\/rsos.220638","volume":"9","author":"P Sanchez","year":"2022","unstructured":"Sanchez, P., et\u00a0al.: Causal machine learning for healthcare and precision medicine. Royal Society Open Science 9(8), 220638 (2022)","journal-title":"Royal Soc. Open Sci."},{"key":"38_CR34","unstructured":"Shalit, U., et\u00a0al.: Estimating individual treatment effect: generalization bounds and algorithms. In: PMLR (2017)"},{"key":"38_CR35","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. CoRR (2018)"},{"key":"38_CR36","unstructured":"Sled, J., et\u00a0al.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. In: TMI (2002)"},{"issue":"34","key":"38_CR37","first-page":"1","volume":"23","author":"W Tang","year":"2022","unstructured":"Tang, W., et\u00a0al.: Soden: A scalable continuous-time survival model through ordinary differential equation networks. The Journal of Machine Learning Research 23(34), 1\u201329 (2022)","journal-title":"J. Mach. Learn. Res."},{"key":"38_CR38","unstructured":"Tousignant, A., et\u00a0al.: Prediction of disease progression in multiple sclerosis patients using deep learning analysis of MRI data. In: Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR (2019)"},{"key":"38_CR39","unstructured":"Verhelst, T., et\u00a0al.: Uplift vs. predictive modeling: a theoretical analysis. arXiv (2023)"},{"issue":"4","key":"38_CR40","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s00415-014-7264-4","volume":"261","author":"TL Vollmer","year":"2014","unstructured":"Vollmer, T.L., et\u00a0al.: A randomized placebo-controlled phase III trial of oral laquinimod for multiple sclerosis. J Neurology (2014)","journal-title":"J. Neurol."},{"key":"38_CR41","unstructured":"Wenao, M., et\u00a0al.: Treatment outcome prediction for intracerebral hemorrhage via generative prognostic model with imaging and tabular data. In: MICCAI (2023)"},{"key":"38_CR42","unstructured":"Xuechen, L., et\u00a0al.: Scalable gradients for stochastic differential equations. CoRR (2020)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72384-1_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:17:59Z","timestamp":1727867879000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72384-1_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723834","9783031723841"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72384-1_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}