Predicting community case transfer path and processing time using decoder models

Yiwen Liu, Dongdong Zhang, Xinyu Yang, Yuanbo Tang, Yang Chen, Qingmin Liao, Yang Li

Abstract: Government agencies and non-profit organizations often rely on case management systems to process the large influx of community request cases. To improve the efficiency of community case management, it's important to model how a community request case is transferred between different departments within the organization and how long it takes to resolve the case. In this paper, we propose two decoder models to predict the departmental transfer path of a given community case and estimate the total processing time based on the predicted path, trained on historical community case records. We compared our prediction results with those obtained using other common machine learning models on a dataset collected from multiple community platforms in Shenzhen, China. Experiments show that our proposed method significantly outperforms the baselines in transfer path and total processing time prediction.