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Text mining and word embedding for classification of decision making variables in breast cancer surgery

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dc.contributor.authorGÜLLÜOĞLU, MAHMUT BAHADIR
dc.contributor.authorsCatanuto G., Rocco N., Maglia A., Barry P., Sgroi G., Russo G., Pappalardo F., Nava M., Heil J., Karakatsanis A., et al.
dc.date.accessioned2022-12-27T08:39:23Z
dc.date.available2022-12-27T08:39:23Z
dc.date.issued2022-07-01
dc.description.abstract© 2022 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical OncologyIntroduction: Decision making in surgical oncology of the breast has increased its complexity over the last twenty years. This Delphi survey investigates the opinion of an expert panel about the decision making process in surgical procedures on the breast for oncological purposes. Methods: Twenty-seven experts were invited to partake into a Delphi Survey. At the first round they have been asked to provide a list of features involved in the decision making process (patient\"s characteristics; disease characteristics; surgical techniques, outcomes) and comment on it. Using text-mining techniques we extracted a list of mono-bi-trigrams potentially representative of decision drivers. A technique of \"natural language processing\" called Word2vec was used to validate changes to texts using synonyms and plesionyms. Word2Vec was also used to test the semantic relevance of n-grams within a corpus of knowledge made up of books edited by panel members. The final list of variables extracted was submitted to the judgement of the panel for final validation at the second round of the Delphi using closed ended questions. Results: 52 features out of 59 have been approved by the panel. The overall consensus was 87.1% Conclusions: Text mining and natural language processing allowed the extraction of a number of decision drivers and outcomes as part of the decision making process in surgical oncology on the breast. This result was obtained transforming narrative texts into structured data. The high level of consensus among experts provided validation to this process.
dc.identifier.citationCatanuto G., Rocco N., Maglia A., Barry P., Sgroi G., Russo G., Pappalardo F., Nava M., Heil J., Karakatsanis A., et al., "Text mining and word embedding for classification of decision making variables in breast cancer surgery", European Journal of Surgical Oncology, cilt.48, sa.7, ss.1503-1509, 2022
dc.identifier.doi10.1016/j.ejso.2022.03.002
dc.identifier.endpage1509
dc.identifier.issn0748-7983
dc.identifier.issue7
dc.identifier.startpage1503
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127825937&origin=inward
dc.identifier.urihttps://hdl.handle.net/11424/284222
dc.identifier.volume48
dc.language.isoeng
dc.relation.ispartofEuropean Journal of Surgical Oncology
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectTıp
dc.subjectDahili Tıp Bilimleri
dc.subjectİç Hastalıkları
dc.subjectOnkoloji
dc.subjectCerrahi Tıp Bilimleri
dc.subjectSağlık Bilimleri
dc.subjectMedicine
dc.subjectInternal Medicine Sciences
dc.subjectInternal Diseases
dc.subjectOncology
dc.subjectSurgery Medicine Sciences
dc.subjectHealth Sciences
dc.subjectKlinik Tıp (MED)
dc.subjectKlinik Tıp
dc.subjectCERRAHİ
dc.subjectONKOLOJİ
dc.subjectClinical Medicine (MED)
dc.subjectCLINICAL MEDICINE
dc.subjectSURGERY
dc.subjectONCOLOGY
dc.subjectCerrahi
dc.subjectSurgery
dc.subjectBreast surgery
dc.subject20-YEAR FOLLOW-UP
dc.subjectCONSERVING SURGERY
dc.subjectMASTECTOMY
dc.subjectIRRADIATION
dc.titleText mining and word embedding for classification of decision making variables in breast cancer surgery
dc.typearticle
dspace.entity.typePublication
local.avesis.id8e8267b2-982e-4bf3-a7bd-7775d4ac4fb7
local.indexed.atWOS
local.indexed.atPUBMED
local.indexed.atSCOPUS
relation.isAuthorOfPublication6d478db0-15a8-4b2b-878f-dc3d1b256022
relation.isAuthorOfPublication.latestForDiscovery6d478db0-15a8-4b2b-878f-dc3d1b256022

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