인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Missing data poses a significant challenge in clinical real-world studies, often arising from unplanned data collection, misplacement, patient loss to follow-up, and other factors. While multiple imputation by chained equations (MICE) is a widely used method, its sequential nature introduces uncertainty, potentially impacting the prediction model performance. We proposed and evaluated three uncertainty-aware functions (i.e., uncertainty sampling (US), probability of improvement (PI), and expected improvement (EI)) integrated with linear regression (LinearReg), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) using three large datasets: chronic kidney disease (CKD, n = 31,043), hypertension cohort from Ramathibodi Hospital (HT-RAMA, n = 140,047) and Khon Kaen University Hospital (HT-KKU, n = 108,942) with high missing rates. In the CKD cohort, uncertainty-aware models significantly improved performance (evaluated by root mean squared error (RMSE) and mean absolute error (MAE)) over standard MICE, except for XGBoost. LinearReg-EI performed best (RMSE 0.12, MAE 0.36), followed by RF-EI (RMSE 0.22, MAE 0.34), and DT-EI (RMSE 0.21, MAE 0.38). In HT-RAMA, LinearReg-US performed best (RMSE 0.24, MAE 8.15), outperforming RF-US (RMSE 0.92, MAE 8.58) and DT-PI (RMSE 0.96, MAE 8.74). Similarly, in HT-KKU, LinearReg-US performed best (RMSE 0.98, MAE 12.00), followed by RF-PI (RMSE 1.93, MAE 12.90) and DT-US (RMSE 2.10, MAE 12.63). Uncertainty-aware models produced imputed distributions closely resembling the original data, unlike standard MICE. Our findings suggest that incorporating uncertainty functions can improve MICE, particularly for LinearReg, RF and DT. Further research is warranted to validate these findings across diverse clinical settings and model types.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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