基于知识图谱的潜在不适当用药预测

林工钞; 滕飞; 胡巧织; 金朝辉; 徐珽; HaiboZhang

doi:10.12182/20230960108

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

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.

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