Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3

Sunho Choi, Insoo Kim, Yoojoong Kim, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pathology is a basic medical field of diagnosing diseases and describing their conditions that are presented in a medical report form. It is important to understand the medical domain from various medical terms in the reports, such as observation parts, disease conditions and names, and measuring units. In this paper, we apply various machine learning algorithms to predict the domain of untrained terms used in real pathological reports. Here, we focus on the oncology section with ICD-O3. The analysis result shows the possibility and potential usefulness of the domain prediction using medical terms.

Original languageEnglish
Title of host publicationICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages520-522
Number of pages3
ISBN (Electronic)9781728113395
DOIs
Publication statusPublished - 2019 Jul 1
Event11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 - Zagreb, Croatia
Duration: 2019 Jul 22019 Jul 5

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2019-July
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Conference

Conference11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
CountryCroatia
CityZagreb
Period19/7/219/7/5

Fingerprint

Oncology
Pathology
Learning algorithms
Learning systems

Keywords

  • ICD-O3
  • Machine Learning
  • Natural Language Processing
  • Pathology
  • Text Classification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

Cite this

Choi, S., Kim, I., Kim, Y., & Seok, J. (2019). Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3. In ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks (pp. 520-522). [8806190] (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICUFN.2019.8806190

Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3. / Choi, Sunho; Kim, Insoo; Kim, Yoojoong; Seok, Junhee.

ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2019. p. 520-522 8806190 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Choi, S, Kim, I, Kim, Y & Seok, J 2019, Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3. in ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks., 8806190, International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2019-July, IEEE Computer Society, pp. 520-522, 11th International Conference on Ubiquitous and Future Networks, ICUFN 2019, Zagreb, Croatia, 19/7/2. https://doi.org/10.1109/ICUFN.2019.8806190
Choi S, Kim I, Kim Y, Seok J. Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3. In ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks. IEEE Computer Society. 2019. p. 520-522. 8806190. (International Conference on Ubiquitous and Future Networks, ICUFN). https://doi.org/10.1109/ICUFN.2019.8806190
Choi, Sunho ; Kim, Insoo ; Kim, Yoojoong ; Seok, Junhee. / Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3. ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2019. pp. 520-522 (International Conference on Ubiquitous and Future Networks, ICUFN).
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