Abstract
This paper presents a decision-support system for document routing in public administration, developed and deployed at the Patient Ombudsman Office in Warsaw, a central government authority in Poland. The proposed solution employs a convolutional neural network extended with a novel decision-making mechanism inspired by glial cells. In biological neural systems, glial cells perform regulatory functions by modulating neuronal activity and influencing the structure of neural connections. An analogous mechanism is incorporated into the model through dynamic control of feature map activity, enabling adaptive modification of a pre-trained network without full retraining. The main contribution of this work is a bio-inspired decision mechanism that allows selective regulation of feature representations in convolutional neural networks. The proposed approach facilitates the identification and elimination of low-informative feature maps, leading to structural optimization of the model. The system automates document routing by analysing document content and supports the processing of several thousand cases per day, significantly enhancing operational efficiency. In addition, the solution reduces document routing time and enables adaptive adjustment of the model to organisational changes. The system has been implemented as a component of an Electronic Document Management (EZD) system and operates in a production environment.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.115
Bio-Inspired Document Routing System for Public Administration
This paper presents a decision-support system for document routing in public administration, developed and deployed at the Patient Ombudsman Office in Warsaw, a central government authority in Poland. The proposed solution employs a convolutional neural network extended with a novel decision-making mechanism inspired by glial cells. In biological neural systems, glial cells perform regulatory functions by modulating neuronal activity and influencing the structure of neural connections. An analogous mechanism is incorporated into the model through dynamic control of feature map activity, enabling adaptive modification of a pre-trained network without full retraining. The main contribution of this work is a bio-inspired decision mechanism that allows selective regulation of feature representations in convolutional neural networks. The proposed approach facilitates the identification and elimination of low-informative feature maps, leading to structural optimization of the model. The system automates document routing by analysing document content and supports the processing of several thousand cases per day, significantly enhancing operational efficiency. In addition, the solution reduces document routing time and enables adaptive adjustment of the model to organisational changes. The system has been implemented as a component of an Electronic Document Management (EZD) system and operates in a production environment.
Recommended Citation
Nowak, J., Maćkiewicz, A., Korytkowski, M., Scherer, R., Budzianowski, P. & Voloshynovskiy, S.(2026). Bio-Inspired Document Routing System for Public Administration. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.115