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Electron Physician. 2018 Jan 25;10(1):6193-6200. doi: 10.19082/6193. eCollection 2018 Jan.

An agent based architecture for high-risk neonate management at neonatal intensive care unit.

Author information

1
Ph.D. Candidate of Medical Informatics, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran.
2
Ph.D. of Health Information Management, Professor, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran.
3
Ph.D. of Biostatistics, Professor, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Science, Tehran, Iran.
4
Ph.D. of Neonatology, Professor, Maternal-Fetal & Neonatal Research Center, Tehran University of Medical Science, Tehran, Iran.
5
Ph.D. of Health Information Management, Assistant Professor, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran.
6
Ph.D. Candidate of Health Information Management, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran.

Abstract

Background:

In recent years, the use of new tools and technologies has decreased the neonatal mortality rate. Despite the positive effect of using these technologies, the decisions are complex and uncertain in critical conditions when the neonate is preterm or has a low birth weight or malformations. There is a need to automate the high-risk neonate management process by creating real-time and more precise decision support tools.

Objective:

To create a collaborative and real-time environment to manage neonates with critical conditions at the NICU (Neonatal Intensive Care Unit) and to overcome high-risk neonate management weaknesses by applying a multi agent based analysis and design methodology as a new solution for NICU management.

Methods:

This study was a basic research for medical informatics method development that was carried out in 2017. The requirement analysis was done by reviewing articles on NICU Decision Support Systems. PubMed, Science Direct, and IEEE databases were searched. Only English articles published after 1990 were included; also, a needs assessment was done by reviewing the extracted features and current processes at the NICU environment where the research was conducted. We analyzed the requirements and identified the main system roles (agents) and interactions by a comparative study of existing NICU decision support systems. The Universal Multi Agent Platform (UMAP) was applied to implement a prototype of our multi agent based high-risk neonate management architecture.

Results:

Local environment agents interacted inside a container and each container interacted with external resources, including other NICU systems and consultation centers. In the NICU container, the main identified agents were reception, monitoring, NICU registry, and outcome prediction, which interacted with human agents including nurses and physicians.

Conclusion:

Managing patients at the NICU units requires online data collection, real-time collaboration, and management of many components. Multi agent systems are applied as a well-known solution for management, coordination, modeling, and control of NICU processes. We are currently working on an outcome prediction module using artificial intelligence techniques for neonatal mortality risk prediction. The full implementation of the proposed architecture and evaluation is considered the future work.

KEYWORDS:

Clinical Decision Support Systems; Health Information Technology; Neonatal Intensive Care; Neonatal Mortality; Software Design

Conflict of interest statement

Conflict of Interest: There is no conflict of interest to be declared.

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