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Labeling Clinical Reports with Active Learning and Topic Modeling
Linköping University, Department of Computer and Information Science, Human-Centered systems.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Uppmärkning av kliniska rapporter med active learning och topic modeller (Swedish)
Abstract [en]

Supervised machine learning models require a labeled data set of high quality in order to perform well. Available text data often exists in abundance, but it is usually not labeled. Labeling text data is a time consuming process, especially in the case where multiple labels can be assigned to a single text document. The purpose of this thesis was to make the labeling process of clinical reports as effective and effortless as possible by evaluating different multi-label active learning strategies. The goal of the strategies was to reduce the number of labeled documents a model needs, and increase the quality of those documents. With the strategies, an accuracy of 89% was achieved with 2500 reports, compared to 85% with random sampling. In addition to this, 85% accuracy could be reached after labeling 975 reports, compared to 1700 reports with random sampling.

Place, publisher, year, edition, pages
2018. , p. 58
Keywords [en]
active learning, topic modeling, topic models, Maximum Loss Reduction with Maximum Confidence, Binary Version Space Minimization, Clinical Reports
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-148463ISRN: LIU-IDA/LITH-EX-A--18/011—SEOAI: oai:DiVA.org:liu-148463DiVA, id: diva2:1216443
External cooperation
Sectra AB
Subject / course
Computer science
Presentation
2018-06-01, 17:27 (English)
Supervisors
Examiners
Available from: 2018-06-13 Created: 2018-06-11 Last updated: 2018-06-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf