Reich DS, Lucchinetti CF, Calabresi PA. Multiple sclerosis. New Engl J Med. 2018;378(2):169–80.
Article
CAS
PubMed
Google Scholar
Rotstein D, Montalban X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis. Nat Rev Neurol. 2019;15(5):287–300.
Article
PubMed
Google Scholar
Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, Correale J, Fazekas F, Filippi M, Freedman MS, Fujihara K, Galetta SL, Hartung HP, Kappos L, Lublin FD, Marrie RA, Miller AE, Miller DH, Montalban X, Mowry EM, Sorensen PS, Tintoré M, Traboulsee AL, Trojano M, Uitdehaag BMJ, Vukusic S, Waubant E, Weinshenker BG, Reingold SC, Cohen JA. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73.
Article
PubMed
Google Scholar
Karabudak R, Dahdaleh M, Aljumah M, Alroughani R, Alsharoqi IA, AlTahan AM, Bohlega SA, Daif A, Deleu D, Amous A, Inshasi JS, Rieckmann P, Sahraian MA, Yamout BI. Functional clinical outcomes in multiple sclerosis: current status and future prospects. Multiple Sclerosis Related Dis. 2015;4(3):192–201.
Article
Google Scholar
Gross RH, Sillau SH, Miller AE, Farrell C, Krieger SC. The multiple sclerosis severity score: fluctuations and prognostic ability in a longitudinal cohort of patients with MS. Multiple Sclerosis J Exp Transl Clin. 2019;5(1):1–8.
Article
Google Scholar
Meyer-Moock S, Feng Y-S, Maeurer M, Dippel F-W, Kohlmann T. 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. BMC Neurol. 2014;14(1):58–58.
Article
PubMed
PubMed Central
Google Scholar
Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89–95.
Article
Google Scholar
Ostmeyer J, Christley S, Rounds WH, Toby I, Greenberg BM, Monson NL, Cowell LG. Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis. BMC Bioinf. 2017;18(1):401–401.
Article
CAS
Google Scholar
Brichetto G, Monti Bragadin M, Fiorini S, Battaglia MA, Konrad G, Ponzio M, Pedullá L, Verri A, Barla A, Tacchino A. The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach. Neurol Sci. 2020;41(2):459–62.
Article
PubMed
Google Scholar
Jackson KC, Sun K, Barbour C, Hernandez D, Kosa P, Tanigawa M, Weideman AM, Bielekova B. Genetic model of MS severity predicts future accumulation of disability. Ann Human Genet. 2020;84(1):1–10.
Article
CAS
Google Scholar
Helland CB, Holmøy T, Gulbrandsen P. Barriers and facilitators related to rehabilitation stays in multiple sclerosis: a qualitative study. Int J MS Care. 2015;17(3):122–9.
Article
PubMed
PubMed Central
Google Scholar
Dennison L, McCloy Smith E, Bradbury K, Galea I. How do people with multiple sclerosis experience prognostic uncertainty and prognosis communication? Qual Study PLoS One. 2016;11(7):0158982–0158982.
Google Scholar
Dennison L, Yardley L, Devereux A, Moss-Morris R. Experiences of adjusting to early stage multiple sclerosis. J Health Psychol. 2011;16(3):478–88.
Article
PubMed
Google Scholar
Desborough J, Brunoro C, Parkinson A, Chisholm K, Elisha M, Drew J, Fanning V, Lueck C, Bruestle A, Cook M, Suominen H, Tricoli A, Henschke A, Phillips C. ‘It struck at the heart of who I thought I was’: a meta-synthesis of the qualitative literature examining the experiences of people with multiple sclerosis. Health Expect. 2020;23(5):1007–27.
Article
PubMed
PubMed Central
Google Scholar
Pétrin J, Donnelly C, McColl M-A, Finlayson M. Is it worth it?: the experiences of persons with multiple sclerosis as they access health care to manage their condition. Health Expect. 2020;23(5):1269–79.
Article
PubMed
PubMed Central
Google Scholar
Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3(3):210–29.
Article
Google Scholar
Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60.
Article
CAS
PubMed
Google Scholar
Mateos-Pérez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: a review of machine learning applications. NeuroImage Clin. 2018;20:506–22.
Article
PubMed
PubMed Central
Google Scholar
Hemond CC, Bakshi R. Magnetic resonance imaging in multiple sclerosis. Cold Spring Harbor Perspectives Med. 2018;8(5): 028969.
Article
CAS
Google Scholar
Zhang Z, Sejdić E. Radiological images and machine learning: trends, perspectives, and prospects. Comput Biol Med. 2019;108:354–70.
Article
PubMed
PubMed Central
Google Scholar
Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62(10):1–34.
Article
Google Scholar
Angelini M, Ferro N, Larsen B, Müller H, Santucci G, Silvello G, Tsikrika T. Measuring and analyzing the scholarly impact of experimental evaluation initiatives. Proc Comput Sci. 2014;38(Supplement C):133–7.
Article
Google Scholar
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial networks. 2014.
Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, Shilton A, Yearwood J, Dimitrova N, Ho TB, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res. 2016;18(12):5870.
Article
Google Scholar
Stevens LM, Mortazavi BJ, Deo RC, Curtis L, Kao DP. Recommendations for reporting machine learning analyses in clinical research. Circul Cardiovasc Qual Outcomes. 2020;13(10): 006556.
Article
Google Scholar
Ahmadi A, Davoudi S, Daliri MR. Computer aided diagnosis system for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention. Comput Methods Programs Biomed. 2019;169:9–18.
Article
PubMed
Google Scholar
Andersen S, Briggs F, Winnike J, Natanzon Y, Maichle S, Knagge K, Newby L, Gregory S. Metabolome-based signature of disease pathology in ms. Multiple Sclerosis Related Dis. 2019;31:12–21.
Article
CAS
Google Scholar
Bertolazzi P, Felici G, Festa P, Fiscon G, Weitschek E. Integer programming models for feature selection: new extensions and a randomized solution algorithm. Eur J Oper Res. 2016;250(2):389–99.
Article
Google Scholar
Broza YY, Har-Shai L, Jeries R, Cancilla JC, Glass-Marmor L, Lejbkowicz I, Torrecilla JS, Yao X, Feng X, Narita A, et al. Exhaled breath markers for nonimaging and noninvasive measures for detection of multiple sclerosis. ACS Chem Neurosci. 2017;8(11):2402–13.
Article
CAS
PubMed
Google Scholar
Chase HS, Mitrani LR, Lu GG, Fulgieri DJ. Early recognition of multiple sclerosis using natural language processing of the electronic health record. BMC Med Inf Decision Making. 2017;17(1):1–8.
Google Scholar
deAndrés-Galiana EJ, Bea G, Fernández-Martínez JL, Saligan LN. Analysis of defective pathways and drug repositioning in multiple sclerosis via machine learning approaches. Comput Biol Med. 2019;115: 103492.
Article
PubMed
CAS
Google Scholar
Galli E, Hartmann FJ, Schreiner B, Ingelfinger F, Arvaniti E, Diebold M, Mrdjen D, van der Meer F, Krieg C, Al Nimer F, et al. Gm-csf and cxcr4 define a t helper cell signature in multiple sclerosis. Nat Med. 2019;25(8):1290–300.
Article
CAS
PubMed
PubMed Central
Google Scholar
Goldstein BA, Polley EC, Briggs FB, Van Der Laan MJ, Hubbard A. Testing the relative performance of data adaptive prediction algorithms: a generalized test of conditional risk differences. Int J Biostat. 2016;12(1):117–29.
Article
PubMed
Google Scholar
Goyal M, Khanna D, Rana PS, Khaiboullina S, Rizvanov A, Baranwal M. Computational intelligence technique for prediction of multiple sclerosis based on serum cytokines. Front Neurol. 2019;10:781.
Article
PubMed
PubMed Central
Google Scholar
Lötsch J, Schiffmann S, Schmitz K, Brunkhorst R, Lerch F, Ferreiros N, Wicker S, Tegeder I, Geisslinger G, Ultsch A. Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy. Sci Rep. 2018;8(1):1–16.
Article
CAS
Google Scholar
Loetsch J, Thrun M, Lerch F, Brunkhorst R, Schiffmann S, Thomas D, Tegder I, Geisslinger G, Ultsch A. Machine-learned data structures of lipid marker serum concentrations in multiple sclerosis patients differ from those in healthy subjects. Int J Mol Sci. 2017;18(6):1217.
Article
CAS
Google Scholar
Perera T, Lee W-L, Yohanandan SA, Nguyen A-L, Cruse B, Boonstra FM, Noffs G, Vogel AP, Kolbe SC, Butzkueven H, et al. Validation of a precision tremor measurement system for multiple sclerosis. J Neurosci Methods. 2019;311:377–84.
Article
PubMed
Google Scholar
Prabahar A, Natarajan J. Prediction of micrornas involved in immune system diseases through network based features. J Biomed Inf. 2017;65:34–45.
Article
Google Scholar
Severini G, Straudi S, Pavarelli C, Da Roit M, Martinuzzi C, Pizzongolo LDM, Basaglia N. Use of nintendo wii balance board for posturographic analysis of multiple sclerosis patients with minimal balance impairment. J Neuroeng Rehabilit. 2017;14(1):19.
Article
Google Scholar
Telalovic JH, Music A. Using data science for medical decision making case: role of gut microbiome in multiple sclerosis. BMC Med Inf Decision Making. 2020;20(1):1–11.
Google Scholar
Torabi A, Daliri MR, Sabzposhan SH. Diagnosis of multiple sclerosis from eeg signals using nonlinear methods. Australasian Phys Eng Sci Med. 2017;40(4):785–97.
Article
Google Scholar
Zhang L, Wang L, Tian P, Tian S. Identification of genes discriminating multiple sclerosis patients from controls by adapting a pathway analysis method. PLoS One. 2016;11(11):0165543.
Article
Google Scholar
Kiiski H, Jollans L, Donnchadha SÓ, Nolan H, Lonergan R, Kelly S, O’Brien MC, Kinsella K, Bramham J, Burke T, et al. Machine learning eeg to predict cognitive functioning and processing speed over a 2-year period in multiple sclerosis patients and controls. Brain Topogr. 2018;31(3):346–63.
Article
PubMed
Google Scholar
Saroukolaei SA, Ghabaee M, Shokri H, Badiei A, Ghourchian S. The role of candida albicans in the severity of multiple sclerosis. Mycoses. 2016;59(11):697–704.
Article
CAS
PubMed
Google Scholar
Sun R, Hsieh KL, Sosnoff JJ. Fall risk prediction in multiple sclerosis using postural sway measures: a machine learning approach. Sci Rep. 2019;9(1):1–7.
CAS
Google Scholar
Bang S, Yoo D, Kim S-J, Jhang S, Cho S, Kim H. Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data. Sci Rep. 2019;9(1):1–9.
Article
CAS
Google Scholar
Guo P, Zhang Q, Zhu Z, Huang Z, Li K. Mining gene expression data of multiple sclerosis. PloS one. 2014;9(6): 100052.
Article
CAS
Google Scholar
Ohanian D, Brown A, Sunnquist M, Furst J, Nicholson L, Klebek L, Jason LA. Identifying key symptoms differentiating myalgic encephalomyelitis and chronic fatigue syndrome from multiple sclerosis. Neurology (E-Cronicon). 2016;4(2):41.
Google Scholar
Ostmeyer J, Christley S, Rounds WH, Toby I, Greenberg BM, Monson NL, Cowell LG. Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis. BMC Bioinf. 2017;18(1):1–10.
Article
CAS
Google Scholar
Azrour S, Piérard S, Geurts P, Van Droogenbroeck M. Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. In: European Symposium on artificial neural networks, computational intelligence and machine learning (ESANN), 2014;649–654.
Fritz NE, Eloyan A, Baynes M, Newsome SD, Calabresi PA, Zackowski KM. Distinguishing among multiple sclerosis fallers, near-fallers and non-fallers. Multiple Sclerosis Related Dis. 2018;19:99–104.
Article
Google Scholar
Gudesblatt M, Srinivasan J, Golan D, Bumstead B, Zarif M, Buhse M, Blitz K, Fafard L, Kantor D, Fratto T, et al. Machine learning models using multi-dimensional digital data and pros predict driving difficulties and falls in people with ms. In: MULTIPLE SCLEROSIS JOURNAL, 2019;vol. 25, pp. 342–343. Sage publications LTD 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
Haider D, Ren A, Fan D, Zhao N, Yang X, Tanoli SAK, Zhang Z, Hu F, Shah SA, Abbasi QH. Utilizing a 5g spectrum for health care to detect the tremors and breathing activity for multiple sclerosis. Trans Emerg Telecommun Technol. 2018;29(10):3454.
Article
Google Scholar
Jackson KC, Sun K, Barbour C, Hernandez D, Kosa P, Tanigawa M, Weideman AM, Bielekova B. Genetic model of ms severity predicts future accumulation of disability. Ann Human Genet. 2020;84(1):1–10.
Article
CAS
Google Scholar
Kosa P, Ghazali D, Tanigawa M, Barbour C, Cortese I, Kelley W, Snyder B, Ohayon J, Fenton K, Lehky T, et al. Development of a sensitive outcome for economical drug screening for progressive multiple sclerosis treatment. Front Neurol. 2016;7:131.
Article
PubMed
PubMed Central
Google Scholar
McGinnis RS, Mahadevan N, Moon Y, Seagers K, Sheth N, Wright JA Jr, DiCristofaro S, Silva I, Jortberg E, Ceruolo M, et al. A machine learning approach for gait speed estimation using skin-mounted wearable sensors: from healthy controls to individuals with multiple sclerosis. PloS one. 2017;12(6):0178366.
Article
CAS
Google Scholar
Morrison C, Huckvale K, Corish B, Banks R, Grayson M, Dorn J, Sellen A, Lindley S. Visualizing ubiquitously sensed measures of motor ability in multiple sclerosis: reflections on communicating machine learning in practice. ACM Trans Interac Intell Syst (TiiS). 2018;8(2):1–28.
Article
CAS
Google Scholar
Shahid AH, Singh M, Kumar G. Severity classification of multiple sclerosis disease: a rough set-based method. Int J Innov Technol Explor Eng. 2019;8(9S):307–14.
Article
Google Scholar
Supratak A, Datta G, Gafson AR, Nicholas R, Guo Y, Matthews PM. Remote monitoring in the home validates clinical gait measures for multiple sclerosis. Front Neurol. 2018;9:561.
Article
PubMed
PubMed Central
Google Scholar
Acquarelli J, Bianchini M, Marchiori E, et al. Discovering potential clinical profiles of multiple sclerosis from clinical and pathological free text data with constrained non-negative matrix factorization. In: European conference on the applications of evolutionary computation, 2016;pp. 169–183. Springer
Fiorini S, Verri A, Tacchino A, Ponzio M, Brichetto G, Barla A. A machine learning pipeline for multiple sclerosis course detection from clinical scales and patient reported outcomes. In: 2015 37th Annual International Conference of the IEEE engineering in medicine and biology society (EMBC), 2015;pp. 4443–4446. IEEE
Gronsbell JL, Cai T. Semi-supervised approaches to efficient evaluation of model prediction performance series b statistical methodology. 2018.
Gupta M, Martens K, Metz LM, de Koning AJ, Pfeffer G. Long noncoding rnas associated with phenotypic severity in multiple sclerosis. Multiple Sclerosis Related Dis. 2019;36: 101407.
Article
Google Scholar
Lim CK, Bilgin A, Lovejoy DB, Tan V, Bustamante S, Taylor BV, Bessede A, Brew BJ, Guillemin GJ. Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression. Sci Rep. 2017;7:41473.
Article
CAS
PubMed
PubMed Central
Google Scholar
Lopez C, Tucker S, Salameh T, Tucker C. An unsupervised machine learning method for discovering patient clusters based on genetic signatures. J Biomed Inf. 2018;85:30–9.
Article
Google Scholar
Bejarano B, Bianco M, Gonzalez-Moron D, Sepulcre J, Goñi J, Arcocha J, Soto O, Del Carro U, Comi G, Leocani L, et al. Computational classifiers for predicting the short-term course of multiple sclerosis. BMC Neurol. 2011;11(1):67.
Article
PubMed
PubMed Central
Google Scholar
Brichetto G, Bragadin MM, Fiorini S, Battaglia MA, Konrad G, Ponzio M, Pedullà L, Verri A, Barla A, Tacchino A. The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach. Neurol Sci. 2020;41(2):459–62.
Article
PubMed
Google Scholar
Briggs FB, Justin CY, Davis MF, Jiangyang J, Fu S, Parrotta E, Gunzler DD, Ontaneda D. Multiple sclerosis risk factors contribute to onset heterogeneity. Multiple Slerosis Related Dis. 2019;28:11–6.
Article
Google Scholar
Flauzino T, Pereira WLdCJ, Alfieri DF, Oliveira SR, Kallaur AP, Lozovoy MAB, Kaimen-Maciel DR, Maes M, Reiche EMV, et al. Disability in multiple sclerosis is associated with age and inflammatory, metabolic and oxidative/nitrosative stress biomarkers: results of multivariate and machine learning procedures. Metabolic Brain Dis. 2019;34(5):1401–13.
Article
CAS
Google Scholar
Pruenza C, Solano MT, Díaz J, Arroyo R, Izquierdo G. Model for prediction of progression in multiple sclerosis. IJIMAI. 2019;5(6):47–53.
Article
Google Scholar
Tacchella A, Romano S, Ferraldeschi M, Salvetti M, Zaccaria A, Crisanti A, Grassi, F. Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study. F1000Research, 2017;6.
Yperman J, Becker T, Valkenborg D, Popescu V, Hellings N, Van Wijmeersch B, Peeters L. Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis. BioRxiv, 772996. 2019.
Zhao Y, Healy BC, Rotstein D, Guttmann CR, Bakshi R, Weiner HL, Brodley CE, Chitnis T. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLoS One. 2017;12(4):0174866.
Article
Google Scholar
Zhao Y, Brodley CE, Chitnis T, Healy BC. Addressing human subjectivity via transfer learning: An application to predicting disease outcome in multiple sclerosis patients. In: Proceedings of the 2014 SIAM International Conference on Data Mining, 2014;pp. 965–973. SIAM
Baranzini SE, Madireddy LR, Cromer A, D’Antonio M, Lehr L, Beelke M, Farmer P, Battaglini M, Caillier SJ, Stromillo ML, et al. Prognostic biomarkers of ifnb therapy in multiple sclerosis patients. Multiple Sclerosis J. 2015;21(7):894–904.
Article
CAS
Google Scholar
Ebrahimkhani S, Beadnall HN, Wang C, Suter CM, Barnett MH, Buckland ME, Vafaee F. Serum exosome micrornas predict multiple sclerosis disease activity after fingolimod treatment. Mol Neurobiol. 2020;57(2):1245–58.
Article
CAS
PubMed
Google Scholar
Fagone P, Mazzon E, Mammana S, Di Marco R, Spinasanta F, Basile MS, Petralia MC, Bramanti P, Nicoletti F, Mangano K. Identification of cd4+ t cell biomarkers for predicting the response of patients with relapsing-remitting multiple sclerosis to natalizumab treatment. Mol Med Rep. 2019;20(1):678–84.
CAS
PubMed
PubMed Central
Google Scholar
Karim ME, Petkau J, Gustafson P, Tremlett H, Group TBS. On the application of statistical learning approaches to construct inverse probability weights in marginal structural cox models: hedging against weight-model misspecification. Commun Stat Simul Comput. 2017;46(10):7668–97.
Article
Google Scholar
Kasatkin D, Bogomolov YV, Spirin N. Steps to personalized therapy of multiple sclerosis: predicting safety of treatment using mathematical modeling. Zhurnal nevrologii i psikhiatrii imeni SS Korsakova. 2018;118(8. Vyp. 2):70–6.
Article
CAS
Google Scholar
Li K, Konofalska U, Akgün K, Reimann M, Rüdiger H, Haase R, Ziemssen T. Modulation of cardiac autonomic function by fingolimod initiation and predictors for fingolimod induced bradycardia in patients with multiple sclerosis. Front Neurosci. 2017;11:540.
Article
PubMed
PubMed Central
Google Scholar
Üçer S, Kocak Y, Ozyer T, Alhajj R. Social network analysis-based classifier (snac): a case study on time course gene expression data. Comput Methods Programs Biomed. 2017;150:73–84.
Article
PubMed
Google Scholar
Walter E, Deisenhammer F. Socio-economic aspects of the testing for antibodies in ms-patients under interferon therapy in austria: a cost of illness study. Multiple Sclerosis Related Dis. 2014;3(6):670–7.
Article
Google Scholar
Patrick MT, Raja K, Miller K, Sotzen J, Gudjonsson JE, Elder JT, Tsoi LC. Drug repurposing prediction for immune-mediated cutaneous diseases using a word-embedding-based machine learning approach. J Invest Dermatol. 2019;139(3):683–91.
Article
CAS
PubMed
Google Scholar
Bhattacharya S, Ramos AGC, Kawsar F, Lane ND, Gionta LM, Manidis J, Silvesti G, Vegreville M. Monitoring daily activities of multiple sclerosis patients with connected health devices. In: Proceedings of the 2018 ACM International Joint Conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers, 2018;666–669.
Papakostas M, Kanal V, Abujelala M, Tsiakas K, Makedon F. Physical fatigue detection through emg wearables and subjective user reports: a machine learning approach towards adaptive rehabilitation. In: Proceedings of the 12th ACM international conference on pervasive technologies related to assistive environments, 2019;475–481.
Chi C, Shao X, Rhead B, Gonzales E, Smith JB, Xiang AH, Graves J, Waldman A, Lotze T, Schreiner T, et al. Admixture mapping reveals evidence of differential multiple sclerosis risk by genetic ancestry. PLoS Genet. 2019;15(1):1007808.
Article
CAS
Google Scholar
Forbes JD, Chen C-Y, Knox NC, Marrie R-A, El-Gabalawy H, de Kievit T, Alfa M, Bernstein CN, Van Domselaar G. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Microbiome. 2018;6(1):1–15.
Article
Google Scholar
Piérard S, Phan-Ba R, Van Droogenbroeck M. Machine learning techniques to assess the performance of a gait analysis system. In: European symposium on artificial neural networks, computational intelligence and machine learning (ESANN), 2014;419–424.
Michel P, Baumstarck K, Loundou A, Ghattas B, Auquier P, Boyer L. Computerized adaptive testing with decision regression trees: an alternative to item response theory for quality of life measurement in multiple sclerosis. Patient Pref Adherence. 2018;12:1043.
Article
Google Scholar
Rezaallah B, Lewis DJ, Pierce C, Zeilhofer H-F, Berg B-I. Social media surveillance of multiple sclerosis medications used during pregnancy and breastfeeding: content analysis. J Med Internet Res. 2019;21(8):13003.
Article
Google Scholar
Deetjen U, Powell JA. Informational and emotional elements in online support groups: a bayesian approach to large-scale content analysis. J Am Med Inf Assoc. 2016;23(3):508–13.
Article
Google Scholar
Kehne JH. The crf1 receptor, a novel target for the treatment of depression, anxiety, and stress-related disorders. CNS Neurol Dis Drug Targets. 2007;6(3):163–82.
Article
CAS
Google Scholar
Arenas-Ramirez N, Woytschak J, Boyman O. Interleukin-2: biology, design and application. Trends Immunol. 2015;36(12):763–77.
Article
CAS
PubMed
Google Scholar
Virdis A, Colucci R, Bernardini N, Blandizzi C, Taddei S, Masi S. Microvascular endothelial dysfunction in human obesity: role of tnf-α. J Clin Endocrinol Metabol. 2019;104(2):341–8.
Article
Google Scholar
Pestian J, Brew C, Matykiewicz P, Hovermale DJ, Johnson N, Cohen KB, Duch W. A shared task involving multi-label classification of clinical free text. In: biological, translational, and clinical language processing, 2007;97–104.
Nagalla R, Pothuganti P, Pawar DS. Analyzing gap acceptance behavior at unsignalized intersections using support vector machines, decision tree and random forests. In: ANT/SEIT, 2017;pp. 474–481.
Kalincik T, Butzkueven H. The MSBase registry: informing clinical practice. Multiple Sclerosis. 2019;25(14):1828–34.
Article
PubMed
Google Scholar
Midaglia L, Mulero P, Montalban X, Graves J, Hauser SL, Julian L, Baker M, Schadrack J, Gossens C, Scotland A, Lipsmeier F, van Beek J, Bernasconi C, Belachew S, Lindemann M. Adherence and satisfaction of smartphone- and smartwatch-based remote active testing and passive monitoring in people with multiple sclerosis: Nonrandomized interventional feasibility study. J Med Internet Res. 2019;21(8):14863.
Article
Google Scholar
Brainteaser: Intelligent Disease Progression Prediction at the Conference and Labs of the Evaluation Forum (CLEF) — IDPP@CLEF 2022. https://brainteaser.health/open-evaluation-challenges/idpp-2022/, last Accessed on 1 March 2022. 2021.
Demner-Fushman D, Elhadad N. Aspiring to unintended consequences of natural language processing: a review of recent developments in clinical and consumer-generated text processing. Yearbook Med Inf. 2016;1:224–33.
Google Scholar
Huang C-C, Lu Z. Community challenges in biomedical text mining over 10 years: Success, failure and the future. Brief Bioinf. 2016;17(1):132–44.
Article
Google Scholar
Filannino M, Uzuner Ö. Advancing the state of the art in clinical natural language processing through shared tasks. Yearbook Med Inf. 2018;27(01):184–92.
Article
Google Scholar
Suominen H, Kelly L, Goeuriot L. Scholarly influence of the conference and labs of the evaluation forum ehealth initiative: review and bibliometric study of the 2012 to 2017 outcomes. JMIR Res Protocols. 2018;7(7):10961. https://doi.org/10.2196/10961.
Article
Google Scholar
Suominen H, Kelly L, Goeuriot L. The scholarly impact and strategic intent of CLEF ehealth labs from 2012 to 2017. In: Ferro N, Peters C, editors. Inf Retrieval Eval Changing World: Lessons Learnfrom 20 Years of CLEF. Cham: Springer; 2019. p. 333–63.
Chapter
Google Scholar
Névéol A, Cohen K, Grouin C, Robert A. Replicability of research in biomedical natural language processing: a pilot evaluation for a coding task. In: Proceedings of the Seventh International workshop on health text mining and information analysis, pp. 78–84. Association for computational linguistics, Austin, TX. 2016.
Cohen KB, Xia J, Zweigenbaum P, Callahan T, Hargraves O, Goss F, Ide N, Névéol A, Grouin C, Hunter LE. Three dimensions of reproducibility in natural language processing. In: Proceedings of the Eleventh International conference on language resources and evaluation (LREC 2018). European language resources Association (ELRA), Miyazaki, Japan. 2018.
Mieskes M, Fort K, Névéol A, Grouin C, Cohen K. Community perspective on replicability in natural language processing. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 768–775. INCOMA Ltd., Varna, Bulgaria. 2019.
Digan W, Névéol A, Neuraz A, Wack M, Baudoin D, Burgun A, Rance B. Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites. J Am Med Inf Assoc. 2020;28(3):504–15.
Article
Google Scholar
Velupillai S, Suominen H, Liakata M, Roberts A, Shah AD, Morley K, Osborn D, Hayes J, Stewart R, Downs J, et al. Using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances. J Biomed Inf. 2018;88:11–9.
Article
Google Scholar
Williamson R. Process and purpose, not thing and technique: How to pose data science research challenges. Harvard data science review. 2020. https://hdsr.duqduq.org/pub/f2cllynw
Ballard DH. Modular learning in neural networks. In: AAAI, 1987;279–284
Ramamurthy V, Yamniuk AP, Lawrence EJ, Yong W, Schneeweis LA, Cheng L, Murdock M, Corbett MJ, Doyle ML, Sheriff S. The structure of the death receptor 4-tnf-related apoptosis-inducing ligand (dr4-trail) complex. Acta Crystallographica Sect F: Struct Biol Commun. 2015;71(10):1273–81.
Article
CAS
Google Scholar
Razzouk R, Shute V. What is design thinking and why is it important. Rev Educ Res. 2012;82(3):330–48.
Article
Google Scholar
Friedman B, Kahn PH, Borning A, Huldtgren A. In: Doorn, N., Schuurbiers, D., van de Poel, I., Gorman, M.E. (eds.) Value sensitive design and information systems, pp. 55–95. Springer, Dordrecht, 2013.
Rashotte J, Tousignant K, Richardson C, Fothergill-Bourbonnais F, Nakhla MM, Olivier P, Lawson ML. Living with sensor-augmented pump therapy in type 1 diabetes: adolescents’ and parents’ search for harmony. Can J Diab. 2014;38(4):256–62.
Article
Google Scholar
Pickup JC, Ford Holloway M, Samsi K. Real-time continuous glucose monitoring in type 1 diabetes: a qualitative framework analysis of patient narratives. Diab Care. 2015;38(4):544–50.
Article
CAS
Google Scholar
Iturralde E, Tanenbaum ML, Hanes SJ, Suttiratana SC, Ambrosino JM, Ly TT, Maahs DM, Naranjo D, Walders-Abramson N, Weinzimer SA, Buckingham BA, Hood KK. Expectations and attitudes of individuals with type 1 diabetes after using a hybrid closed loop system. Diab Educ. 2017;43(2):223–32.
Article
Google Scholar
Lawton J, Blackburn M, Allen J, Campbell F, Elleri D, Leelarathna L, Rankin D, Tauschmann M, Thabit H, Hovorka R. Patients’ and caregivers’ experiences of using continuous glucose monitoring to support diabetes self-management: qualitative study. BMC End Dis. 2018;18(1):12–12.
Article
CAS
Google Scholar
Ceuninck van Capelle Ad, Meide Hvd, Vosman FJH, Visser LH. A qualitative study assessing patient perspectives in the process of decision-making on disease modifying therapies (dmt’s) in multiple sclerosis (ms). PLOS ONE. 2017;12(8):1–10. https://doi.org/10.1371/journal.pone.0182806.
Article
CAS
Google Scholar
Henschke A, Desborough J, Parkinson A, Brunoro C, Fanning V, Lueck C, Brew-Sam N, Brüstle A, Drew J, Chisholm K, et al. Personalizing medicine and technologies to address the experiences and needs of people with multiple sclerosis. J Personal Med. 2021;11(8):791.
Article
Google Scholar