使用人工智能技术进行个性化venlafaxine剂量预测:基于真实世界数据的回顾性分析
Yimeng Liu1,2, Ze Yu3, Xuxiao Ye4
1Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People's Republic of China.
个性化venlafaxine剂量是至关重要的. 一个使用真实世界的数据的新人工智能模型准确地预测了基于患者血度和年龄等因素的最佳venlafaxine剂量.
科学领域:
- 药物基因组学和精准医学
- 医疗保健中的人工智能
- 临床药学和药理学 临床药学和药理学
背景情况:
- 文拉法辛的剂量在患者之间有很大差异,需要个性化治疗策略.
- 优化venlafaxine剂量对于最大限度地提高治疗疗效和最大限度地减少不良影响至关重要.
研究的目的:
- 通过使用现实世界的数据,确定影响文拉法辛剂量要求的关键因素.
- 开发和验证一个基于人工智能的模型,用于预测个性化的venlafaxine剂量.
主要方法:
- 对接受venlafaxine治疗的抑郁症患者的回顾性分析.
- 用于剂量预测的七个机器学习模型 (XGBoost,LightGBM,CatBoost,GBDT,ANN,TabNet,DT) 的比较.
- 使用混矩阵和接收器操作特征 (ROC) 分析进行验证.
主要成果:
- 塔布网模型实现了最高的预测准确度 (0.80).
- 确定了七个重要变量:血液中的文拉法辛度,总蛋白质,淋巴细胞,年龄,血球蛋白,胆化酶和血小板数.
- 曲线下的高面积 (AUC) 值用于预测75毫克 (0.90),150毫克 (0.85) 和225毫克 (0.90) 的剂量.
结论:
- 通过使用真实世界的数据,成功开发了一种强大的TabNet模型来预测venlafaxine剂量.
- 该模型表现出高精度,支持个性化的venlafaxine剂量方案.
- 这些发现为优化拉法辛治疗提供了有价值的临床指导.
更多相关视频
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
相关概念视频
Nonlinear Pharmacokinetics: Dependence of Elimination Half-Life and Dose Clearance
A study on guinea pigs examined the...
Antidepressant Drugs: MAOIs and Other Agents
Antidepressant Drugs: Overview
Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Dosage Regimen: Fixed Dose
Fixed-dose regimens can be used for various routes of administration, including intravenous (IV) injections and oral medications. For IV administration, a predetermined amount of the drug is...
Pharmacovigilance
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
