Trung tâm Thông tin - Thư viện Trường Đại học Y Dược Hải Phòng

NLM	B13
Tác giả CN	Trần Thị Ngân
Nhan đề	Developing a machine learning-based predictive model for Levothyroxine dosage estimation in hypothyroid patients: a retrospective study
Thông tin xuất bản	2025
Thông tin xuất bản	Frontiers Media SA
Tóm tắt	Among the models tested, the Extra Trees Regressor (ETR) demonstrated the highest predictive accuracy, achieving an R² of 87.37% and the lowest mean absolute error of 9.4 mcg (95% CI: 7.7-11.2) in the test set. Other ensemble models, including Random Forest and Gradient Boosting, also showed strong performance (R² > 80%). Feature importance analysis highlighted BMI (0.516 ± 0.015) as the most influential predictor, followed by comorbidities (0.120 ± 0.010) and age (0.080 ± 0.005). The findings underscore the potential of machine learning in refining LT4 dose estimation by incorporating diverse clinical factors beyond traditional weight-based approaches. The model provides a solid foundation for personalized LT4 dosing, which can enhance treatment precision and reduce the risk of under- or over-medication.
Thuật ngữ chủ đề	Pharmacology
Từ khóa tự do	Endocrine; Hypothyroidism
Tác giả(bs) CN	Nguyễn Thị Thu Phương
Địa chỉ	100Kho Bài báo quốc tế(1): BQT00053
Tệp tin điện tử	https://pubmed.ncbi.nlm.nih.gov/41180363/
Tệp tin điện tử	https://lib.hpmu.edu.vn/kiposdata2/tapchi2026/anhbiatc/biabbqt_thumbimage.jpg

MARC

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Tag	Giá trị
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001	33969
002	13
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005	202605140919
008	081223s2025 vm\| vie
009	1 0
039	[ ] \|a 20260514091911 \|b phuongntt \|y 20260514091800 \|z phuongntt
041	[ ] \|a Eng
084	[ ] \|a B13
100	[ ] \|a Trần Thị Ngân
245	[ ] \|a Developing a machine learning-based predictive model for Levothyroxine dosage estimation in hypothyroid patients: a retrospective study
260	[ ] \|c 2025
260	[ ] \|b Frontiers Media SA
520	[ ] \|a Among the models tested, the Extra Trees Regressor (ETR) demonstrated the highest predictive accuracy, achieving an R² of 87.37% and the lowest mean absolute error of 9.4 mcg (95% CI: 7.7-11.2) in the test set. Other ensemble models, including Random Forest and Gradient Boosting, also showed strong performance (R² > 80%). Feature importance analysis highlighted BMI (0.516 ± 0.015) as the most influential predictor, followed by comorbidities (0.120 ± 0.010) and age (0.080 ± 0.005). The findings underscore the potential of machine learning in refining LT4 dose estimation by incorporating diverse clinical factors beyond traditional weight-based approaches. The model provides a solid foundation for personalized LT4 dosing, which can enhance treatment precision and reduce the risk of under- or over-medication.
650	[ ] \|a Pharmacology
653	[ ] \|a Endocrine
653	[ ] \|a Hypothyroidism
700	[ ] \|a Nguyễn Thị Thu Phương
773	[ ] \|t Frontiers in Endocrinology
852	[ ] \|a 100 \|b Kho Bài báo quốc tế \|j (1): BQT00053
856	[ ] \|u https://pubmed.ncbi.nlm.nih.gov/41180363/
856	[1 ] \|u https://lib.hpmu.edu.vn/kiposdata2/tapchi2026/anhbiatc/biabbqt_thumbimage.jpg
890	[ ] \|a 1 \|b 0 \|c 0 \|d 0

Dòng	Mã vạch	Bản sao	Nơi lưu	Tình trạng	Cho phép yêu cầu
1	BQT00053	1	Kho Bài báo quốc tế
#1 BQT00053 Nơi lưu Kho Bài báo quốc tế Tình trạng

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