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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한임상검사정도관리협회 Journal of Laboratory Medicine And Quality Assurance Laboratory Medicine and Quality Assurance 제46권 제3호
- 발행연도
- 2024.9
- 수록면
- 167 - 173 (7page)
이용수
초록· 키워드
오류제보하기
Platelet function tests (PFTs) are essential for predicting bleeding tendencies and assessing the effectiveness of antiplatelet agents. The VerifyNow System is a quick and simple PFT, but warning messages (WMs) due to biological factors may reduce its effectiveness. This study aimed to quantify the frequency of WMs and evaluate the performance of machine learning (ML) models for predicting these WMs. Data were retrospectively collected from patients who underwent VeryNow System testing from October 2019 to April 2023. The patients were classified into WM-positive (WMPOS) and WM-negative (WMNEG) groups. Significant variables between two groups were selected for feature analysis. Prediction models were developed using XGBoost, random forest (RF), support vector machine, and logistic regression algorithms. A receiver operating characteristic (ROC) curve analysis with five-fold crossvalidation was performed using Python (Python Software Foundation, USA). A total of 6,998 data were collected from 6,438 patients, with 0.8% (55/6,998) classified as WMPOS. Significant differences were observed in sex, alanine transaminase levels, alkaline phosphatase levels, total bilirubin levels, creatinine levels, prothrombin time, white blood cell count, and hematocrit count between the two groups. The area under the ROC of the four models for predicting the WMPOS showed excellent or good (0.8–1.0) performance. Both XGBoost and RF models achieved accuracy, precision, recall, and F1 scores exceeding 0.99. Machine learning models were used to predict the WMs of PFT and showed good performance, potentially enhancing the efficiency of PFT. However, further research is needed to apply ML in clinical laboratories.
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