Chronic Heart Failure Follow-up Management Based on Agent Technology
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초록
Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. Methods: This research was performed in 2013–2014 to determine appropriate scenarios and the data required to monitor and followup CHF patients, and then an agent-based model was designed. Results: Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. Conclusions: The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making.
Abstract
Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. Methods: This research was performed in 2013–2014 to determine appropriate scenarios and the data required to monitor and followup CHF patients, and then an agent-based model was designed. Results: Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. Conclusions: The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making.
참고문헌 (14)
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