학술논문
머신러닝과 공통데이터모델을 활용한 국가 간암 검진 대상자의 간암 예측 모델
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- 영문명
- A Prediction Model for Surveillance Patients of Liver Cancer using Common Data Model and Machine Learning
- 발행기관
- 한국보건사회약료경영학회
- 저자명
- 간행물 정보
- 『한국보건사회약료경영학회지』제10권 제1호, 4~13쪽, 전체 10쪽
- 주제분류
- 의약학 > 기타의약학
- 파일형태
- 발행일자
- 2022.05.31
4,000원
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이 학술논문 정보는 (주)교보문고와 각 발행기관 사이에 저작물 이용 계약이 체결된 것으로, 교보문고를 통해 제공되고 있습니다.
국문 초록
영문 초록
BACKGROUNDS To find early liver cancer, the ministry of health and welfare has conducted surveillance targeting high-risk patients. In 2017, the incidence rate of liver cancer in surveillance was 0.9%, suggesting that a broad patient group was included in surveillance. In this study, to reduce surveillance patients, a prediction model with zero-falsenegative was developed using a machine learning.
METHODS To develop the model, we used 2016 Health Insurance Review & Assessment Service-National Patients Sample utilized to the Common Data Model (CDM). This study targeted patients who did not have a severe condition of liver cancer in surveillance. The number of the target was 13,703 cases. The covariates for the model were identified by a chi-square test conducted on gender, age group, condition between a case and control group. LASSO was performed to develop the model.
RESULTS Gender, age group, forty diseases were selected as a covariate. The model has an AUC of 0.745, a negative rate of 4.0%, a specificity of 4.5%, and a PPV of 11.8% with zerofalse-negative.
CONCLUSION It might be possible to refine surveillance and save the budget of the National Health Insurance Service, and governments.
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