Evaluating sequence-to-sequence models for simulating medical staff mobility on time

Abstract The process of improving medical care effectiveness requires approaches for optimizing hospitality staff scheduling. Speaking of nursing care, their scheduling is directly connected with intra-hospital dynamics. In this paper, we present generator model for nurses’ dynamics event chains based on encoder-decoder neural network. The model takes timestamp sequences with arbitrary lengths as input and produces sequences of places visited by simulated nurse. Our model is trained on physical access control system (PACS) data obtained from Almazov National Medical Research Centre. The number of methods for improving model accuracy were used. It was shown that trained generator produces realistic sequences with low level of hallucinations, opening new perspective of applications sequence-to-sequence models for system simulation.