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Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and recorded in unstructured text in the form of a neurology consult note.
Diagnosis and management of multiple sclerosis
Peter A. Calabresi. 2004 · 1944
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Medicine, Computers, and Linguistics
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Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS)
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Classification of Clinical Conditions : A Case Study on Prediction of Obesity and Its Co-morbidities
Archana Bhattarai, Vasile Rus, and Dipankar Dasgupta. 2009 · 2009
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Multiple sclerosis review
Marvin M. Goldenberg. 2012 · 2012
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Natural Language Processing, Electronic Health Records, and Clinical Research
F Liu, H Yu, C Weng, Feifan Liu, Chunhua Weng, and Hong Yu. 2012 · 2012
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Semi-supervised clinical text classification with laplacian svms: An application to cancer case management
Vijay Garla, Caroline Taylor, and Cynthia Brandt. 2013 · 2013
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Sex and gender issues in multiple sclerosis
Hanne F. Harbo, Ralf Gold, and Mar Tintora. 2013 · 2013
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Distributed representations ofwords and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corraudo, and Jeffrey Dean. 2013 · 2013
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis
Sandra Meyer-Moock, You Shan Feng, Mathias Maeurer, Franz Werner Dippel, and Thomas Kohlmann. 2014 · 2014
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Text de-identification for privacy protection: A study of its impact on clinical text information content
Stéphane M. Meystre, Óscar Ferrández, F. Jeffrey Friedlin, Brett R. South, Shuying Shen, and Matthew H. Samore. 2014 · 2014
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Spatial Analysis of Global Prevalence of Multiple Sclerosis Suggests Need for an Updated Prevalence Scale
Brett J. Wade. 2014 · 2014
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The intelligent use and clinical benefits of electronic medical records in multiple sclerosis
Mary F. Davis and Jonathan L. Haines. 2015 · 2015
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A comparison of models for predicting early hospital readmissions
Joseph Futoma, Jonathan Morris, and Joseph Lucas. 2015 · 2015
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The Canadian survey of health, lifestyle and ageing with multiple sclerosis: methodology and initial results.[Erratum appears in BMJ Open. 2015;5(3):e005718; PMID: 25757943]
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Ye Zhang and Byron C. Wallace. 2015 · 2015
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Multi-layer Representation Learning for Medical Concepts
Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, and Jimeng Sun. 2016a · 2016
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Deep ehr: a survey of recent advances in deep learning techniques for electronic health record (ehr) analysis
Benjamin Shickel, Patrick James Tighe, Azra Bihorac, and Parisa Rashidi. 2017 · 2017
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Clinical Named Entity Recognition Using Deep Learning Models
Yonghui Wu, Min Jiang, Jun Xu, Degui Zhi, and Hua Xu. 2017 · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Explainable Prediction of Medical Codes from Clinical Text
James Mullenbach, Sarah Wiegreffe, Jon Duke, Jimeng Sun, and Jacob Eisenstein. 2018 · 2018
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What We Learned from The History of Multiple Sclerosis Measurement: Expanded Disease Status Scale
Bilge Piri Cinar and Yuksel Guven Yorgun. 2018 · 2018
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Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
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Clinical Information Extraction via Convolutional Neural Network
Peng Li and Heng Huang. 2016 · 2016
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Data Programming: Creating Large Training Sets, Quickly
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, and Christopher Ré. 2016 · 2016
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Relation extraction from clinical texts using domain invariant convolutional neural network
Sunil Kumar Sahu, Ashish Anand, Krishnadev Oruganty, and Mahanandeeshwar Gattu. 2016 · 2016
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Patient subtyping via time-aware lstm networks
Inci M Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K Jain, and Jiayu Zhou. 2017 · 2017
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What’s in a Note? Unpacking Predictive Value in Clinical Note Representations
Willie Boag, Dustin Doss, Tristan Naumann, and Peter Szolovits. 2018 · 2017
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An rnn architecture with dynamic temporal matching for personalized predictions of parkinson’s disease
Chao Che, Cao Xiao, Jian Liang, Bo Jin, Jiayu Zho, and Fei Wang. 2017a · 2017
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Using recurrent neural network models for early detection of heart failure onset
Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2017 · 2017
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Publicly available clinical BERT embeddings
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Scibert: Pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
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Harnessing electronic medical records to advance research on multiple sclerosis
Vincent Damotte, Antoine Lizée, Matthew Tremblay, Alisha Agrawal, Pouya Khankhanian, Adam Santaniello, Refujia Gomez, Robin Lincoln, Wendy Tang, Tiffany Chen, Nelson Lee, Pablo Villoslada, Jill A Hollenbach, Carolyn D Bevan, Jennifer Graves, Riley Bove, Douglas S Goodin, Ari J Green, Sergio E Baranzini, Bruce Ac Cree, Roland G Henry, Stephen L Hauser, Jeffrey M Gelfand, and Pierre-Antoine Gourraud. 2019 · 2019
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 2019
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Yifan Peng, Shankai Yan, and Zhiyong Lu. 2019 · 2019
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Training complex models with multi-task weak supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré. 2019 · 2019
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Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2020 · 2020
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