liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Word embeddings and Patient records: The identification of MRI risk patients
Linköpings universitet, Institutionen för datavetenskap.
2019 (Engelska)Självständigt arbete på grundnivå (kandidatexamen), 12 poäng / 18 hpStudentuppsats (Examensarbete)
Abstract [en]

Identification of risks ahead of MRI examinations is identified as a cumbersome and time-consuming process at the Linköping University Hospital radiology clinic. The hospital staff often have to search through large amounts of unstructured patient data to find information about implants. Word embeddings has been identified as a possible tool to speed up this process. The purpose of this thesis is to evaluate this method, and that is done by training a Word2Vec model on patient journal data and analyzing the close neighbours of key search words by calculating cosine similarity. The 50 closest neighbours of each search words are categorized and annotated as relevant to the task of identifying risk patients ahead of MRI examinations or not. 10 search words were explored, leading to a total of 500 terms being annotated. In total, 14 different categories were observed in the result and out of these 8 were considered relevant. Out of the 500 terms, 340 (68%) were considered relevant. In addition, 48 implant models could be observed which are particularly interesting because if a patient have an implant, hospital staff needs to determine it’s exact model and the MRI conditions of that model. Overall these findings points towards a positive answer for the aim of the thesis, although further developments are needed.

Ort, förlag, år, upplaga, sidor
2019. , s. 22
Nyckelord [en]
word2vec, word embeddings, patient records, MRI safety, digital healthcare
Nationell ämneskategori
Språkteknologi (språkvetenskaplig databehandling)
Identifikatorer
URN: urn:nbn:se:liu:diva-157467ISRN: LIU-IDA/KOGVET-G--19/002--SEOAI: oai:DiVA.org:liu-157467DiVA, id: diva2:1324363
Externt samarbete
Cambio Healthcare Systems
Ämne / kurs
Kognitionsvetenskap
Handledare
Examinatorer
Tillgänglig från: 2019-06-14 Skapad: 2019-06-13 Senast uppdaterad: 2019-06-14Bibliografiskt granskad

Open Access i DiVA

fulltext(525 kB)60 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 525 kBChecksumma SHA-512
f459ed27c527bce0924d02eaf624f7fee52c01f5a54066dd115e36cce58bcbb22eb4966e32655ce21d980ae822afcab6aa386beaa5965c7dc5795ec93f8feb2f
Typ fulltextMimetyp application/pdf

Sök vidare i DiVA

Av författaren/redaktören
Kindberg, Erik
Av organisationen
Institutionen för datavetenskap
Språkteknologi (språkvetenskaplig databehandling)

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 60 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 375 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf