메뉴 건너뛰기
Library Notice
Institutional Access
If you certify, you can access the articles for free.
Check out your institutions.
ex)Hankuk University, Nuri Motors
Log in Register Help KOR
Subject

The Analysis of Adequacy on Cancer-related Medical articles Using Machine learning
Recommendations
Search
Questions

머신러닝을 활용한 암 관련 의료 기사 데이터의 적절성 분석

논문 기본 정보

Type
Proceeding
Author
Junyoung Park (성균관대학교) Minsik Lee (가톨릭대학교) Imryung Kim (삼성서울병원) Junghee Yoon (삼성서울병원) Juhee Cho (성균관대학교) Jundong Cho (성균관대학교)
Journal
The HCI Society of Korea 한국HCI학회 학술대회 PROCEEDINGS OF HCI KOREA 2016 학술대회 발표 논문집
Published
2016.1
Pages
454 - 459 (6page)
DOI
10.17210/hcik.2016.01.454

Usage

cover
📌
Topic
📖
Background
🔬
Method
🏆
Result
The Analysis of Adequacy on Cancer-related Medical articles Using Machine learning
Ask AI
Recommendations
Search
Questions

Abstract· Keywords

Report Errors
While national cancer incidence and death toll steadily increased, patients are receiving information through several media to find information that will help in their treatment. The number of cancer-related medical articles in the media to meet these needs and its influence has increased. However, some articles have insufficient scientific evidence and exaggerate drug’s effectiveness and there is also increasing in articles to promote a particular hospital or medicines. These articles cause problems to provide wrong information to the patients. In hospitals, doctors classify articles in order to provide the correct information to their patients but, people do not visit hospital have difficulty to obtain the information classified by doctors. This study is to analyze the appropriateness of cancer-related medical articles published during the year 2014, we propose a system for classifying an article that provides right information to the patient.

Contents

요약문
ABSTRACT
1. 연구배경
2. 관련연구
3. 연구방법
4. 결론 및 논의
참고문헌

References (0)

Add References

Recommendations

It is an article recommended by DBpia according to the article similarity. Check out the related articles!

Related Authors

Frequently Viewed Together

Recently viewed articles

Comments(0)

0

Write first comments.