Deng Yihan, Denecke Kerstin
Department of Technology and Computer Science, Institute for Medical Informatics, Bern University of Applied Sciences, Biel/Bienne, Switzerland.
Front Artif Intell. 2022 Nov 4;5:1000283. doi: 10.3389/frai.2022.1000283. eCollection 2022.
The Swiss classification of surgical interventions (CHOP) has to be used in daily practice by physicians to classify clinical procedures. Its purpose is to encode the delivered healthcare services for the sake of quality assurance and billing. For encoding a procedure, a code of a maximal of 6-digits has to be selected from the classification system, which is currently realized by a rule-based system composed of encoding experts and a manual search in the CHOP catalog. In this paper, we will investigate the possibility of automatic CHOP code generation based on a short query to enable automatic support of manual classification. The wide and deep hierarchy of CHOP and the differences between text used in queries and catalog descriptions are two apparent obstacles for training and deploying a learning-based algorithm. Because of these challenges, there is a need for an appropriate classification approach. We evaluate different strategies (multi-class non-terminal and per-node classifications) with different configurations so that a flexible modular solution with high accuracy and efficiency can be provided. The results clearly show that the per-node binary classification outperforms the non-terminal multi-class classification with an F1-micro measure between 92.6 and 94%. The hierarchical prediction based on per-node binary classifiers achieved a high exact match by the single code assignment on the 5-fold cross-validation. In conclusion, the hierarchical context from the CHOP encoding can be employed by both classifier training and representation learning. The hierarchical features have all shown improvement in the classification performances under different configurations, respectively: the stacked autoencoder and training examples aggregation using true path rules as well as the unified vocabulary space have largely increased the utility of hierarchical features. Additionally, the threshold adaption through Bayesian aggregation has largely increased the vertical reachability of the per node classification. All the trainable nodes can be triggered after the threshold adaption, while the F1 measures at code levels 3-6 have been increased from 6 to 89% after the threshold adaption.
医生在日常实践中必须使用瑞士外科手术干预分类法（CHOP）对临床程序进行分类。其目的是为了质量保证和计费对所提供的医疗服务进行编码。为了对一个程序进行编码，必须从分类系统中选择一个最多6位数字的代码，目前这是通过一个由编码专家组成的基于规则的系统和在CHOP目录中进行手动搜索来实现的。在本文中，我们将研究基于简短查询自动生成CHOP代码的可能性，以实现对手动分类的自动支持。CHOP的广泛而深入的层次结构以及查询中使用的文本与目录描述之间的差异是训练和部署基于学习的算法的两个明显障碍。由于这些挑战，需要一种合适的分类方法。我们评估了不同配置下的不同策略（多类非终端分类和每个节点分类），以便提供一个具有高精度和效率的灵活模块化解决方案。结果清楚地表明，每个节点的二元分类优于非终端多类分类，F1微测度在92.6%至94%之间。基于每个节点二元分类器的层次预测在5折交叉验证中通过单个代码分配实现了高精确匹配。总之，CHOP编码的层次上下文可用于分类器训练和表示学习。层次特征在不同配置下的分类性能均有提高：堆叠自动编码器和使用真实路径规则的训练示例聚合以及统一词汇空间大大提高了层次特征的效用。此外，通过贝叶斯聚合进行的阈值调整大大提高了每个节点分类的垂直可达性。阈值调整后所有可训练节点都能被触发，而阈值调整后代码级别3-6的F1度量从6%提高到了89%。
