{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T23:49:14Z","timestamp":1777852154712,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Nordic eHealth for Patients: Benchmarking and Developing for the Future","award":["100477"],"award-info":[{"award-number":["100477"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>Guillain-Barr\u00e9 Syndrome (GBS) is a neurological disorder affecting people of any age and sex, mainly damaging the peripheral nervous system. GBS is divided into several subtypes, in which only four are the most common, demanding different treatments. Identifying the subtype is an expensive and time-consuming task. Early GBS detection is crucial to save the patient\u2019s life and not aggravate the disease. This work aims to provide a primary screening tool for GBS subtypes fast and efficiently without complementary invasive methods, based only on clinical variables prospected in consultation, taken from clinical history, and based on risk factors. We conducted experiments with four classifiers with different approaches, five different filters for feature selection, six wrappers, and One versus All (OvA) classification. For the experiments, we used a data set that includes 129 records of Mexican patients and 26 clinical representative variables. Random Forest filter obtained the best results in each classifier for the diagnosis of the four subtypes, in the same way, this filter with the SVM classifier achieved the best result (0.6840). OvA with SVM classifier reached a balanced accuracy of 0.8884 for the Miller-Fisher (MF) subtype.<\/jats:p>","DOI":"10.1177\/14604582211021471","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T05:53:46Z","timestamp":1629266026000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Toward a machine learning model for a primary diagnosis of Guillain-Barr\u00e9 syndrome subtypes"],"prefix":"10.1177","volume":"27","author":[{"given":"Daniel","family":"Alarc\u00f3n-Narv\u00e1ez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Hern\u00e1ndez-Torruco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Betania","family":"Hern\u00e1ndez-Oca\u00f1a","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0324-9886","authenticated-orcid":false,"given":"Oscar","family":"Ch\u00e1vez-Bosquez","sequence":"additional","affiliation":[{"name":"Universidad Juarez Autonoma de Tabasco, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jerusa","family":"Marchi","sequence":"additional","affiliation":[{"name":"Universidade Federal de Santa Catarina, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Jos\u00e9","family":"M\u00e9ndez-Castillo","sequence":"additional","affiliation":[{"name":"Hospital Psiqui\u00e1trico de Campeche, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"bibr1-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1111\/jns.12096"},{"key":"bibr2-14604582211021471","unstructured":"Instituto Mexicano del Seguro Social. Diagn\u00f3stico y tratamiento: S\u00edndrome de Guillain-Barr\u00e9, segundo y tercer nivel de atenci\u00f3n, http:\/\/www.cenetec.salud.gob.mx\/descargas\/gpc\/CatalogoMaestro\/089_GPC_SxGBarre2y3NA\/GuillainBarrE_EVR_CENETEC.pdf (2016, accessed 15 July 2019)."},{"key":"bibr3-14604582211021471","unstructured":"World Health Organization. Identification and treatment of Guillain-Barr\u00e9 syndrome in the context of the Zika virus outbreak, https:\/\/www.who.int\/csr\/resources\/publications\/zika\/guillain-barre-syndrome\/en\/ (2016, accessed 16 September 2019)."},{"key":"bibr4-14604582211021471","unstructured":"GBS\/CIDP Fundation International. Guillain-Barr\u00e9 syndrome, CIDP and variants: an overview for the layperson, https:\/\/www.gbs-cidp.org\/wp-content\/uploads\/2014\/09\/Overview-for-the-Layperson-ENGLISH.pdf (2010, accessed 07 September 2019)."},{"issue":"1","key":"bibr5-14604582211021471","first-page":"35","volume":"39","author":"Panesar K","year":"2014","journal-title":"US Pharm"},{"key":"bibr6-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2012.01.025"},{"key":"bibr7-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2014.121"},{"key":"bibr8-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77244-8"},{"key":"bibr9-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2016.12.005"},{"key":"bibr10-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2014.11.005"},{"key":"bibr11-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.04.224"},{"key":"bibr12-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2013.11.016"},{"key":"bibr13-14604582211021471","first-page":"1012","volume-title":"2015 7th International IEEE\/EMBS conference on neural engineering (NER)","author":"Masood A"},{"key":"bibr14-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0177726"},{"issue":"5","key":"bibr15-14604582211021471","first-page":"162","volume":"14","author":"Hern\u00e1ndez-Torruco J","year":"2013","journal-title":"Rev Mex Neuroci"},{"key":"bibr16-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/8424198"},{"key":"bibr17-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/1576927"},{"key":"bibr18-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780040112"},{"key":"bibr19-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/432109"},{"key":"bibr20-14604582211021471","first-page":"30","volume-title":"2009 IEEE 17th International Conference on Program Comprehension","author":"Hindle A"},{"key":"bibr21-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1007\/BF00993309"},{"issue":"8","key":"bibr22-14604582211021471","first-page":"33","volume":"12","author":"Hassanat A","year":"2014","journal-title":"Int J Comput Sci Inf Secur"},{"key":"bibr23-14604582211021471","volume-title":"Statistical learning theory","author":"Vapnik V","year":"1998","edition":"1"},{"key":"bibr24-14604582211021471","first-page":"463","volume-title":"2018 3rd International conference on computer science and engineering (UBMK)","author":"Keles MK"},{"key":"bibr25-14604582211021471","author":"Romanski P","year":"2018","journal-title":"CRAN Repository"},{"issue":"170","key":"bibr26-14604582211021471","first-page":"1","volume":"17","author":"Bischl B","year":"2016","journal-title":"J Mach Learn Res"},{"key":"bibr27-14604582211021471","first-page":"101","volume":"5","author":"Rifkin R","year":"2004","journal-title":"J Mach Learn Res"},{"key":"bibr28-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1007\/s40484-016-0081-2"},{"key":"bibr29-14604582211021471","volume-title":"Data mining: practical machine learning tools and techniques","author":"Witten I","year":"2011","edition":"3"},{"key":"bibr30-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7"},{"issue":"2","key":"bibr31-14604582211021471","first-page":"56","volume":"19","author":"Mendoza-Hern\u00e1ndez D","year":"2010","journal-title":"Alerg Asma Inmunol Pediatr"},{"key":"bibr32-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awt285"},{"key":"bibr33-14604582211021471","doi-asserted-by":"publisher","DOI":"10.1157\/13058172"}],"container-title":["Health Informatics Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582211021471","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14604582211021471","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582211021471","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:31:35Z","timestamp":1777501895000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14604582211021471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10.1177\/14604582211021471"],"URL":"https:\/\/doi.org\/10.1177\/14604582211021471","relation":{},"ISSN":["1460-4582","1741-2811"],"issn-type":[{"value":"1460-4582","type":"print"},{"value":"1741-2811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7]]},"article-number":"14604582211021471"}}