Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification.

Journal: Journal of biomedical informatics

Published Date: Jun 1, 2018

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 the acute coronary syndrome (ACS) was developed and used in an experimental study.

Authors

Sergey V Kovalchuk

ITMO University, Saint Petersburg, Russia. Electronic address: kovalchuk@mail.ifmo.ru.
Anastasia A Funkner

ITMO University, Saint Petersburg, Russia.
Oleg G Metsker

ITMO University, Saint Petersburg, Russia.
Aleksey N Yakovlev

ITMO University, Saint Petersburg, Russia; Almazov National Medical Research Centre, Saint Petersburg, Russia. Electronic address: yakovlev_an@almazovcentre.ru.

Keywords

Acute Coronary Syndrome Cloud Computing Cluster Analysis Computer Simulation Critical Pathways Data Mining Electronic Health Records Humans Machine Learning Medical Informatics Russia Workflow

External Resources

View on PubMed Access via DOI PubMed (29753874)

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification.

Abstract

Authors

Keywords

External Resources

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