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The Diabetic Patient Agent [electronic resource] : Modeling Disease in Humans and the Healthcare System Response / by Raman Paranjape, Zhanle (Gerald) Wang, Simerjit Gill.

By: Paranjape, Raman [author.].
Contributor(s): Wang, Zhanle (Gerald) [author.] | Gill, Simerjit [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 133Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XIV, 123 p. 81 illus., 74 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662562918.Subject(s): Computational intelligence | Diseases | Biomedical engineering | Artificial intelligence | Medical informatics | Public health administration | Computational Intelligence | Diseases | Biomedical Engineering and Bioengineering | Artificial Intelligence | Health Informatics | Health AdministrationAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction.- Agent-based Modeling and Simulation.- The Ackerman Mathematical Model.- Patient-physician interaction model.- Simulation Results.- Control patient Agent Model -- Self Aware patient Agent Model -- Blood Glucose Monitoring Frequency Evaluation.- Conclusions and future work.
In: Springer Nature eBookSummary: This book provides a pioneering approach to modeling the human diabetic patient using a software agent. It is based on two MASc (Master of Applied Science) theses: one looking at the evolution of the patient agent in time, and another looking the interaction of the patient agent with the healthcare system. It shows that the software agent evolves in a manner analogous to the human patient and exhibits typical attributes of the illness such as reacting to food consumption, medications, and activity. This agent model can be used in a number of different ways, including as a prototype for a specific human patient with the purpose of helping to identify when that patient’s condition deviates from normal variations. The software agent can also be used to study the interaction between the human patient and the health care system. This book is of interest to anyone involved in the management of diabetic patients or in societal research into the management of diabetes. The diabetic patient agent was developed using the Ackerman model for diabetes, but this model can be easily adapted for any other model subject with the necessary physiological data to support that model.
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Introduction.- Agent-based Modeling and Simulation.- The Ackerman Mathematical Model.- Patient-physician interaction model.- Simulation Results.- Control patient Agent Model -- Self Aware patient Agent Model -- Blood Glucose Monitoring Frequency Evaluation.- Conclusions and future work.

This book provides a pioneering approach to modeling the human diabetic patient using a software agent. It is based on two MASc (Master of Applied Science) theses: one looking at the evolution of the patient agent in time, and another looking the interaction of the patient agent with the healthcare system. It shows that the software agent evolves in a manner analogous to the human patient and exhibits typical attributes of the illness such as reacting to food consumption, medications, and activity. This agent model can be used in a number of different ways, including as a prototype for a specific human patient with the purpose of helping to identify when that patient’s condition deviates from normal variations. The software agent can also be used to study the interaction between the human patient and the health care system. This book is of interest to anyone involved in the management of diabetic patients or in societal research into the management of diabetes. The diabetic patient agent was developed using the Ackerman model for diabetes, but this model can be easily adapted for any other model subject with the necessary physiological data to support that model.

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