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Development and External Validation of a Machine Learning Model for Predicting Drug- Induced Immune Thrombocytopenia in a Real-World Hospital Cohort

Development and External Validation of a Machine Learning Model for Predicting Drug- Induced Immune Thrombocytopenia in a Real-World Hospital Cohort

 Nguyễn Thị Thu Phương
 Springer Nature Link 2025
 Eng
PharmacologyPredicting DrugThrombocytopenia
Mô tả biểu ghi
ID:33970
NLM B13
Tác giả CN Nguyễn Thị Thu Phương
Nhan đề Development and External Validation of a Machine Learning Model for Predicting Drug- Induced Immune Thrombocytopenia in a Real-World Hospital Cohort
Thông tin xuất bản 2025
Thông tin xuất bản Springer Nature Link
Tóm tắt Among 17,546 patients in the training cohort and 1,403 in the external cohort, DITP occurred in 432 (2.46%) and 70 (4.99%) patients, respectively. In internal validation, LightGBM achieved an AUC of 0.860, recall of 0.392, and F1-score of 0.310. External validation confirmed model robustness with an AUC of 0.813 and an F1-score of 0.341 at the optimized threshold (0.09). SHAP analysis identified AST, baseline platelet count, and renal function as key contributors. DCA and clinical impact curves demonstrated potential benefit in supporting real-time risk stratification. Clopidogrel and vancomycin were frequently associated with suspected DITP cases.
Thuật ngữ chủ đề Pharmacology
Từ khóa tự do Predicting Drug; Thrombocytopenia
Tác giả(bs) CN Vũ Mạnh Tân
Tác giả(bs) CN Nguyễn Thị Thu Phương
Địa chỉ 100Kho Bài báo quốc tế(1): BQT00054
Tệp tin điện tử https://link.springer.com/article/10.1186/s12911-025-03107-3
Tệp tin điện tử https://lib.hpmu.edu.vn/kiposdata2/tapchi2026/anhbiatc/biabbqt_thumbimage.jpg
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041[ ] |a Eng
084[ ] |a B13
100[ ] |a Nguyễn Thị Thu Phương
245[ ] |a Development and External Validation of a Machine Learning Model for Predicting Drug- Induced Immune Thrombocytopenia in a Real-World Hospital Cohort
260[ ] |c 2025
260[ ] |b Springer Nature Link
520[ ] |a Among 17,546 patients in the training cohort and 1,403 in the external cohort, DITP occurred in 432 (2.46%) and 70 (4.99%) patients, respectively. In internal validation, LightGBM achieved an AUC of 0.860, recall of 0.392, and F1-score of 0.310. External validation confirmed model robustness with an AUC of 0.813 and an F1-score of 0.341 at the optimized threshold (0.09). SHAP analysis identified AST, baseline platelet count, and renal function as key contributors. DCA and clinical impact curves demonstrated potential benefit in supporting real-time risk stratification. Clopidogrel and vancomycin were frequently associated with suspected DITP cases.
650[ ] |a Pharmacology
653[ ] |a Predicting Drug
653[ ] |a Thrombocytopenia
700[ ] |a Vũ Mạnh Tân
700[ ] |a Nguyễn Thị Thu Phương
773[ ] |t Frontiers in Endocrinology
852[ ] |a 100 |b Kho Bài báo quốc tế |j (1): BQT00054
856[ ] |u https://link.springer.com/article/10.1186/s12911-025-03107-3
856[1 ] |u https://lib.hpmu.edu.vn/kiposdata2/tapchi2026/anhbiatc/biabbqt_thumbimage.jpg
890[ ] |a 1 |b 0 |c 0 |d 0
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1 BQT00054 1 Kho Bài báo quốc tế
#1 BQT00054
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