基于多标签学习的创伤救治层链决策研究

Layer Chains Decision of Trauma Treatment Based on Multi-label Learning

在现代创伤救治中,根据患者伤情进行合理而准确的院前评估并制定相应的救治决策对降低患者伤残率与死亡率具有重要意义。为了改善人工制定决策的缺陷,实现准确合理的标准化创伤救治决策制定,本文利用多标签学习思想,在对创伤救治决策进行深入分析与研究的基础上,将整体救治决策进行子决策划分,并提取出子决策对应的判定因素作为标签集。为了更好地考虑标签间的关联,将Classifier Chains算法的链式思想与多标签K近邻(Multi-label K-nearest neighbor,ML-KNN)算法融合,提出一种层链多标签学习算法,称为层链多标签K近邻算法(Layer chain ML-KNN,LCML-KNN)。LCML-KNN算法将标签依特点划分为两个层链,在第一层链的预测标签信息输出后对其进行独热编码,转化后的标签看作新特征放入第二层链进行预测与判断。LCML-KNN算法不仅更好地考虑了标签间的关联性,而且通过标签转化扩充了特征维数。在两个创伤类数据集上与现有各类多标签算法进行实验对比,结果验证了LCML-KNN算法的鲁棒性和优越性。

In modern trauma treatment,reasonable and accurate pre-hospital assessment based on the injury and making corresponding treatment decisions are of great significance for reducing the disability and mortality of patients.To improve the shortcomings of manual decision-making and achieve accurate and reasonable standardized trauma treatment decision-making,after in-depth analysis and research on the treatment decision,this study uses the multi-label learning method to divide the overall treatment decision into sub-decisions,and extracts judgment factors corresponding to the sub-decisions as a label sets.Next,to better consider the relationship between labels,this paper combines the chain idea of the Classifier Chains algorithm with the ML-KNN algorithm,and proposes a multi-label learning algorithm by improving the ML-KNN algorithm,named layer chains multi-label K-nearest neighbor(LCML-KNN).The LCMLKNN algorithm divides labels into two layer chains according to the characteristics.After the prediction label information of the first layer chain is output,it is uniquely encoded.And the transformed lables are put into the second layer chain as new features for prediction and judgement.The LCML-KNN algorithm not only better takes into account the relationship between the labels but also expands the feature dimension through the label conversion.The experimental results with various existing multi-label learning algorithms on two trauma datasets verify the robustness and superiority of the LCML-KNN algorithm.