Abstract. Introduction: An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of acute coronary syndrome (ACS) was developed and used within an experimental study. Methods: Combination of data, text, and process mining techniques and machine learning approaches for analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enable identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others). The proposed approach enables more realistic and detailed simulation of the patient flow within a group of related departments. Experimental study shows that the improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Federal Almazov North-west Medical Research Centre in Saint Petersburg, Russia. Conclusion: The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for implementation of simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.
In REF , a process extraction and machine learning approach for electronic health record (EHR) analysis with discrete event simulation (DES) and queuing theory was proposed.
Simulation of Patient Flow in Multiple Healthcare Units using Process and Data Mining Techniques for Model Identification
ArXiv
ArXiv
Medicine Computer Science