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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.4
- 수록면
- 480 - 490 (11page)
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초록· 키워드
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Nowadays, Electronic Medical Record (EMR) has just implemented at few hospitals for Outpatient Department (OPD). OPD is the diversified data, it includes demographic and diseases of patient, so it need to be clustered in order to explore the hidden rules and the relationship of data types of patient"s information. In this paper, we propose a novel approach for unsupervised clustering of patient"s demographic and diseases in OPD. Firstly, we collect data from a hospital at OPD. Then, we preprocess and transform data by using powerful techniques such as standardization, label encoder, and categorical encoder. After obtaining transformed data, we use some strong experiments, techniques, and evaluation to select the best number of clusters and best clustering algorithm. In addition, we use some tests and measurements to analyze and evaluate cluster tendency, models, and algorithms. Finally, we obtain the results to analyze and discover new knowledge, meanings, and rules. Clusters that are found out in this research provide knowledge to medical managers and doctors. From these information, they can improve the patient management methods, patient arrangement methods, and doctor’s ability. In addition, it is a reference for medical data scientist to mine OPD dataset.
목차
ABSTRACT
1. INTRODUCTION
2. APPROACH
3. PERFORMANCE EVALUATION
4. RESULT INTERPRETATION
5. CONCLUSION
REFERENCE
참고문헌 (16)
참고문헌 신청
주문일 / 2016 / 생체신호 습득과 건강 모니터링을 위한스마트 헬스케어 의복 개발 / 멀티미디어학회논문지 19(5):808~817
M. Chen / 2017 / Disease Prediction by Machine Learning Over Big Data from Healthcare Communities / IEEE Access 5:8869~8879
The Hexagon / Korea helps Sri Lanka Install a Hospital Health Information System
N. M. Saravana Kumar / 2015 / Predictive Methodology for Diabetic Data Analysis in Big Data / Procedia Computer Science 50:203~208
R. Miotto / 2016 / Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
M. Chen / 2017 / Disease Prediction by Machine Learning Over Big Data from Healthcare Communities / IEEE Access 5:8869~8879
The Hexagon / Korea helps Sri Lanka Install a Hospital Health Information System
N. M. Saravana Kumar / 2015 / Predictive Methodology for Diabetic Data Analysis in Big Data / Procedia Computer Science 50:203~208
R. Miotto / 2016 / Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
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UCI(KEPA) : I410-ECN-0101-2019-004-000895291