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Development of cancer pathology data model and natural language processing based data conversion methodology
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 박, 래웅 | - |
| dc.contributor.author | 신, 다혜 | - |
| dc.date.accessioned | 2021-01-06T02:34:59Z | - |
| dc.date.available | 2021-01-06T02:34:59Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/19262 | - |
| dc.description.abstract | 비공개 | - |
| dc.description.tableofcontents | I. Introduction 1
A. Study Background and Necessity 1 1. The Current Cancer Incidence in Korea 1 2. Importance of Cancer Pathology Report 3 3. Status of converting medical data into structured data based on Natural Language Processing method 4 4. The necessity of modeling and unifying cancer pathology data 8 B. Study Purpose 12 II. Materials and Method 14 A. Study Subject data 17 B. Analysis of system and vocabulary of the cancer pathology report 18 C. Development of Cancer Pathology Data Model 21 D. Development of natural language processing model for data extraction 27 1. Text preprocessing 33 2. Named Entity Recognition Model Structure 42 3. Model training 44 4. Realization of rule-based algorithm 47 E. Model evaluation method 50 F. Design of common data model conversion 51 III. Result 59 A. Establishment of Korean cancer pathology reports system and vocabulary dictionary 59 B. Comparison of Model performance for data extraction 67 1. Comparison of performance by parameter setting 67 2. Comparison of performance between CNN model and Hybrid (CNN+Rule-based) model 72 C. Development of data conversion methodology for the structured cancer pathology report 76 1. Establishment of clinical data model for cancer pathology report 79 2. Result evaluation through manual review of data 83 3. Establishment of common data model 84 IV. Discussion 88 A. Consideration on study method and result 88 B. Study limitations 97 V. Conclusion 99 References 101 | - |
| dc.language.iso | en | - |
| dc.title | Development of cancer pathology data model and natural language processing based data conversion methodology | - |
| dc.title.alternative | 암 병리 데이터 모델 및 자연어처리기반 데이터 변환 방법론 개발 | - |
| dc.type | Thesis | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000030250 | - |
| dc.subject.keyword | Cancer Pathology report | - |
| dc.subject.keyword | Natural Language Processing | - |
| dc.subject.keyword | Named Entity Recognition | - |
| dc.subject.keyword | Convolutional neural network | - |
| dc.subject.keyword | Data Modeling | - |
| dc.subject.keyword | Distributed Research Network | - |
| dc.subject.keyword | Common Data Model | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | 대학원 의학과 | - |
| dc.contributor.affiliatedAuthor | 신, 다혜 | - |
| dc.date.awarded | 2020 | - |
| dc.type.local | Theses | - |
| dc.citation.date | 2020 | - |
| dc.embargo.liftdate | 9999-12-31 | - |
| dc.embargo.terms | 9999-12-31 | - |
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