Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3

Sunho Choi; Insoo Kim; Yoojoong Kim; Junhee Seok

doi:10.1109/ICUFN.2019.8806190

Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3

Sunho Choi, Insoo Kim, Yoojoong Kim, Junhee Seok

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	520-522
Number of pages	3
ISBN (Electronic)	9781728113395
DOIs	https://doi.org/10.1109/ICUFN.2019.8806190
Publication status	Published - 2019 Jul
Event	11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 - Zagreb, Croatia Duration: 2019 Jul 2 → 2019 Jul 5

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2019-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
Country/Territory	Croatia
City	Zagreb
Period	19/7/2 → 19/7/5

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

Access to Document

10.1109/ICUFN.2019.8806190

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 et al.
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 proceeding › Conference 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

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title = "Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3",

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.",

keywords = "ICD-O3, Machine Learning, Natural Language Processing, Pathology, Text Classification",

author = "Sunho Choi and Insoo Kim and Yoojoong Kim and Junhee Seok",

note = "Funding Information: This work was supported by the grants of the NRF of Korea (NRF-2017R1C1B2002850) and KHIDI1(HI19C0360, HI19C0201). The correspondence should be addressed to jseok14@korea.ac.kr Publisher Copyright: {\textcopyright} 2019 IEEE.; 11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 ; Conference date: 02-07-2019 Through 05-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ICUFN.2019.8806190",

language = "English",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

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N1 - Funding Information: This work was supported by the grants of the NRF of Korea (NRF-2017R1C1B2002850) and KHIDI1(HI19C0360, HI19C0201). The correspondence should be addressed to jseok14@korea.ac.kr Publisher Copyright: © 2019 IEEE.

PY - 2019/7

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N2 - 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.

AB - 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.

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KW - Machine Learning

KW - Natural Language Processing

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BT - ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks

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Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3

Abstract

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Keywords

ASJC Scopus subject areas

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