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dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorBORGES-JUNIOR, Renato
dc.contributor.authorSALVINI, Rogerio
dc.contributor.authorNIERENBERG, Andrew A.
dc.contributor.authorSACHS, Gary S.
dc.contributor.authorLAFER, Beny
dc.contributor.authorDIAS, Rodrigo S.
dc.date.accessioned2019-04-24T18:18:15Z-
dc.date.available2019-04-24T18:18:15Z-
dc.date.issued2018
dc.identifier.citationPROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), p.613-616, 2018
dc.identifier.isbn978-1-5386-5488-0
dc.identifier.issn2156-1125
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/31462-
dc.description.abstractBipolar disorder (BD) is a mood disorder characterized by recurrent episodes of depression and mania/hypomania. Depressive relapse in BD reach rates close to 50% in 1 year and 70% in up to 4 years of treatment. Several studies have been developed to discover more efficient treatments for BD and prevent relapses. However, most of relapse studies used only statistical methods. We aim to analyze the performance of machine learning algorithms in predicting depressive relapse using only clinical data from patients. Five well-used machine learning algorithms (Support Vector Machines, Random Forests, Naive Bayes and Multilayer Perceptron) were applied to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) dataset of a cohort of 800 patients who became euthymic during the study and were followed up for 1 year: 507 presented a depressive relapse and 293 did not. The algorithms showed reasonable performance in the prediction task, ranging from 61% to 80% in the F-measure. Random Forest algorithm had a higher average of performance (Relapse Group 68%; No Relapse Group 74%), although, the performance between classifiers showed no significant difference. Random Forest analysis demonstrated that the three most important mood symptoms observed were: interest, depression mood and energy. Results show that the machine learning algorithms could be seen as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.eng
dc.description.sponsorshipCAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior)
dc.language.isoeng
dc.publisherIEEEeng
dc.relation.ispartofProceedings 2018 Ieee International Conference on Bioinformatics and Biomedicine (bibm)
dc.relation.ispartofseriesIEEE International Conference on Bioinformatics and Biomedicine-BIBM
dc.rightsrestrictedAccesseng
dc.subjectbipolar disordereng
dc.subjectmental healtheng
dc.subjectdepressive relapseeng
dc.subjectmachine learningeng
dc.subjectartificial intelligenceeng
dc.subject.othertreatment enhancement programeng
dc.subject.otherrecurrenceeng
dc.subject.otherprevalenceeng
dc.subject.otherrateseng
dc.titleForecasting depressive relapse in Bipolar Disorder from clinical dataeng
dc.typeconferenceObjecteng
dc.rights.holderCopyright IEEEeng
dc.description.conferencedateDEC 03-06, 2018
dc.description.conferencelocalMadrid, SPAIN
dc.description.conferencenameIEEE International Conference on Bioinformatics and Biomedicine (BIBM)
dc.subject.wosComputer Science, Interdisciplinary Applicationseng
dc.subject.wosMathematical & Computational Biologyeng
dc.type.categoryproceedings papereng
dc.type.versionpublishedVersioneng
hcfmusp.author.externalBORGES-JUNIOR, Renato:Univ Fed Goias, Inst Informat, Goiania, Go, Brazil
hcfmusp.author.externalSALVINI, Rogerio:Univ Fed Goias, Inst Informat, Goiania, Go, Brazil
hcfmusp.author.externalNIERENBERG, Andrew A.:Harvard Med Sch, Massachusetts Gen Hosp, Dept Psychiat, Boston, MA 02115 USA
hcfmusp.author.externalSACHS, Gary S.:Harvard Med Sch, Massachusetts Gen Hosp, Dept Psychiat, Boston, MA 02115 USA
hcfmusp.description.beginpage613
hcfmusp.description.endpage616
hcfmusp.origemWOS
hcfmusp.origem.idWOS:000458654000103
hcfmusp.origem.id2-s2.0-85062505283
hcfmusp.publisher.cityNEW YORKeng
hcfmusp.publisher.countryUSAeng
hcfmusp.relation.referenceChawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953eng
hcfmusp.relation.referenceDeckersbach T., 2016, J AFFECTIVE DISORDEReng
hcfmusp.relation.referenceDem J., 2006, J MACH LEARN RES, V7, P1eng
hcfmusp.relation.referenceFerrari AJ, 2016, BIPOLAR DISORD, V18, P440, DOI 10.1111/bdi.12423eng
hcfmusp.relation.referenceGoldstein BA, 2017, J AM MED INFORM ASSN, V24, P198, DOI 10.1093/jamia/ocw042eng
hcfmusp.relation.referenceGoodwin FK, 2007, MANIC DEPRESSIVE ILL, V1eng
hcfmusp.relation.referenceHall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI [10.1145/1656274.1656278, DOI 10.1145/1656274.1656278]eng
hcfmusp.relation.referenceKitchenham B., 2004, KEELE U TECH REP, V33, P1eng
hcfmusp.relation.referenceKohavi R., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, P1137eng
hcfmusp.relation.referenceLibrenza-Garcia D, 2017, NEUROSCI BIOBEHAV R, V80, P538, DOI 10.1016/j.neubiorev.2017.07.004eng
hcfmusp.relation.referencePerlis RH, 2006, AM J PSYCHIAT, V163, P217, DOI 10.1176/appi.ajp.163.2.217eng
hcfmusp.relation.referencePerlis RH, 2009, BIPOLAR DISORD, V11, P391, DOI 10.1111/j.1399-5618.2009.00686.xeng
hcfmusp.relation.referenceSachs GS, 2003, BIOL PSYCHIAT, V53, P1028, DOI 10.1016/S0006-3223(03)00165-3eng
hcfmusp.relation.referenceSalvini R, 2015, STUD HEALTH TECHNOL, V216, P741, DOI 10.3233/978-1-61499-564-7-741eng
hcfmusp.relation.referenceShim IH, 2015, J AFFECT DISORDERS, V173, P120, DOI 10.1016/j.jad.2014.10.061eng
hcfmusp.relation.referenceVazquez GH, 2015, EUR NEUROPSYCHOPHARM, V25, P1501, DOI 10.1016/j.euroneuro.2015.07.013eng
dc.description.indexPubMedeng
dc.identifier.eissn2156-1133
hcfmusp.citation.scopus4-
hcfmusp.scopus.lastupdate2023-01-06-
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