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 |
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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 | |
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 |
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Volume | 2019-July |
ISSN (Print) | 2165-8528 |
ISSN (Electronic) | 2165-8536 |
Conference
Conference | 11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 |
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Country/Territory | Croatia |
City | Zagreb |
Period | 19/7/2 → 19/7/5 |
Bibliographical 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 [email protected]
Publisher Copyright:
© 2019 IEEE.
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