基于知识图谱的潜在不适当用药预测
Knowledge Graph-Based Prediction of Potentially Inappropriate Medication
-
摘要:目的 为提高潜在不适当用药(potentially inappropriate medication, PIM)预测的准确率,提出一种结合知识图谱和机器学习的PIM预测模型。方法 首先,基于2019版Beers标准,以知识图谱为基本结构,构建具有逻辑表达能力的PIM知识表示体系,实现从患者信息到PIM的推理过程。其次,利用分类器链算法建立每个PIM标签的机器学习预测模型,从数据中学习潜在特征关联。最后,根据样本量阈值,将知识图谱的部分推理结果作为分类器链上的输出标签,增加低频PIM预测结果的可靠性。结果 实验采用来自成都地区9家医疗机构的11741份处方数据,对模型有效性进行评估。实验表明,该模型对于PIM数量预测的准确率为98.10%,F1值为93.66%,对于PIM多标签预测的汉明损失为0.06%,macro-F1为66.09%,与现有模型相比有着更高的预测精度。结论 该PIM预测模型具有更好的潜在不适当用药预测性能,并且对于低频PIM标签识别效果提升显著。Abstract:Objective To improve the accuracy of potentially inappropriate medication (PIM) prediction, a PIM prediction model that combines knowledge graph and machine learning was proposed.Methods Firstly, based on Beers criteria 2019 and using the knowledge graph as the basic structure, a PIM knowledge representation framework with logical expression capabilities was constructed, and a PIM inference process was implemented from patient information nodes to PIM nodes. Secondly, a machine learning prediction model for each PIM label was established based on the classifier chain algorithm, to learn the potential feature associations from the data. Finally, based on a threshold of sample size, a portion of reasoning results from the knowledge graph was used as output labels on the classifier chain to enhance the reliability of the prediction results of low-frequency PIMs.Results 11741 prescriptions from 9 medical institutions in Chengdu were used to evaluate the effectiveness of the model. Experimental results show that the accuracy of the model for PIM quantity prediction is 98.10%, the F1 is 93.66%, the Hamming loss for PIM multi-label prediction is 0.06%, and the macroF1 is 66.09%, which has higher prediction accuracy than the existing models.Conclusion The method proposed has better prediction performance for potentially inappropriate medication and significantly improves the recognition of low-frequency PIM labels.
© 2023 《koko体育app
学报(医学版)》编辑部 版权所有
开放获取൲ 本文遵循知识共享署名—非商业性使用4.0国际许可协议(CC BY-NC 4.0),允许第三方对本刊发表的论文自由共享(即在任何媒介以任何形式复制、发行原文)、演绎(即修改、转换或以原文为基础进行创作),必须给出适当的署名,提供指向本文许可协议的链接,同时标明是否对原文作了修改;不得将本文用于商业目的。CC BY-NC 4.0许可协议详情请访问