Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier

Deng, Yihan; Denecke, Kerstin (2022). Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier Frontiers in Artificial Intelligence, 5(5) Frontiers Research Foundation 10.3389/frai.2022.1000283

frontierts_CHOP_classification.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (2MB) | Preview

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.

Item Type:

Journal Article (Original Article)


School of Engineering and Computer Science > Institute for Patient-centered Digital Health
School of Engineering and Computer Science


Deng, Yihan and
Denecke, Kerstin0000-0001-6691-396X


Q Science > Q Science (General)
T Technology > T Technology (General)




Frontiers Research Foundation




Kerstin Denecke

Date Deposited:

04 Nov 2022 10:59

Last Modified:

25 Oct 2023 13:52

Publisher DOI:


Uncontrolled Keywords:

ensemble classifier, CHOP, hierarchical classification, medical procedure, feature selection




Actions (login required)

View Item View Item
Provide Feedback