基于人工智能的青少年舍曲林剂量预测模型:一项真实世界研究。

IF 3.6 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Ran Fu, Ze Yu, Chunhua Zhou, Jinyuan Zhang, Fei Gao, Donghan Wang, Xin Hao, Xiaolu Pang, Jing Yu
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引用次数: 0

摘要

背景舍曲林的药代动力学参数存在个体差异,在青少年中尤为明显。我们旨在基于人工智能(AI)技术建立抑郁症青少年舍曲林的个体化给药模型:收集了2019年12月至2022年7月期间在河北医科大学第一医院接受治疗的258名青少年患者的数据。采用9种不同的算法进行建模,比较对舍曲林日剂量的预测能力,包括XGBoost、LGBM、CatBoost、GBDT、SVM、ANN、TabNet、KNN和DT。对四个剂量分组(50 毫克、100 毫克、150 毫克和 200 毫克)的性能进行了分析:结果:CatBoost 被选为性能最佳的个体化用药模型。结果发现有六个重要变量与舍曲林剂量相关,包括血浆浓度、PLT、MPV、GL、A/G 和 LDH。ROC曲线和混淆矩阵显示CatBoost模型在四个剂量亚组中具有良好的预测性能(50 mg、100 mg、150 mg和200 mg的AUC分别为0.93、0.81、0.93和0.93):基于人工智能的舍曲林青少年抑郁症剂量预测模型具有良好的预测能力,可为临床医生提出最佳用药方案提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study.

Background: Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques.

Methods: Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed.

Results: CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively).

Conclusion: The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.

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来源期刊
Expert Review of Clinical Pharmacology
Expert Review of Clinical Pharmacology PHARMACOLOGY & PHARMACY-
CiteScore
7.30
自引率
2.30%
发文量
127
期刊介绍: Advances in drug development technologies are yielding innovative new therapies, from potentially lifesaving medicines to lifestyle products. In recent years, however, the cost of developing new drugs has soared, and concerns over drug resistance and pharmacoeconomics have come to the fore. Adverse reactions experienced at the clinical trial level serve as a constant reminder of the importance of rigorous safety and toxicity testing. Furthermore the advent of pharmacogenomics and ‘individualized’ approaches to therapy will demand a fresh approach to drug evaluation and healthcare delivery. Clinical Pharmacology provides an essential role in integrating the expertise of all of the specialists and players who are active in meeting such challenges in modern biomedical practice.
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