Automatic Generation of Agents using Reusable Soft Computing Code Libraries to develop Multi Agent System for Healthcare

Full Text (PDF, 494KB), PP.48-54

Views: 0 Downloads: 0

Author(s)

Priti Srinivas Sajja 1,*

1. Department of Computer Science, Sardar Patel University, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2015.05.07

Received: 3 Aug. 2014 / Revised: 10 Dec. 2014 / Accepted: 19 Jan. 2015 / Published: 8 Apr. 2015

Index Terms

Multi Agent, Neural Network, Fuzzy Logic, Neuro-Fuzzy Hybridization, Healthcare

Abstract

This paper illustrates architecture for a multi agent system in healthcare domain. The architecture is generic and designed in form of multiple layers. One of the layers of the architecture contains many proactive, co-operative and intelligent agents such as resource management agent, query agent, pattern detection agent and patient management agent. Another layer of the architecture is a collection of libraries to auto-generate code for agents using soft computing techniques. At this stage, codes for artificial neural network and fuzzy logic are developed and encompassed in this layer. The agents use these codes for development of neural network, fuzzy logic or hybrid solutions such as neuro-fuzzy solution. Third layer encompasses knowledge base, metadata and other local databases. The multi layer architecture is supported by personalized user interfaces for friendly interaction with its users. The framework is generic, flexible, and designed for a distributed environment like the Web; with minor modifications it can be employed on grid or cloud platform. The paper also discusses detail design issues, suitable applications and future enhancement of the work.

Cite This Paper

Priti Srinivas Sajja, "Automatic Generation of Agents using Reusable Soft Computing Code Libraries to develop Multi Agent System for Healthcare", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.5, pp.48-54, 2015. DOI:10.5815/ijitcs.2015.05.07

Reference

[1]N. Ganesan, K. Venkatesh, and M.A. Rama. “Application of neural networks in diagnosing cancer disease using demographic data,” International Journal of Computer Applications, vol.1(26), pp.76-85, 2010.

[2]A. Filippo, L. Alberto, M. Eladia, M. Peña, V. Petr, H. Aleš, and H. Josef. “Artificial neural networks in medical diagnosis,” Journal of Applied Biomedicine, vol.11, pp.47-58, 2013.

[3]E. Alkim, E. Gürbüz, and E. Kiliç. “A fast and adaptive automated disease diagnosis method with an innovative neural network model,” Neural Networks, vol. 33, pp.88-96, 2012.

[4]A. Barwad, P. Dey, and S. Susheilia. “Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology,” Cytometry B Clyn Cytom, vol. 82, pp.107-111, 2012.

[5]M. Catalogna, E. Cohen, S. Fishman, Z. Halpern, U. Nevo, and E. Ben-Jacob. “Artificial neural networks based controller for glucose monitoring during clamp test,” PloS One. Vol.7(8), pp.e44587, 2012. 

[6]P. Dey, A. Lamba, S. Kumari, and N. Marwaha. “Application of an artificial neural network in the prognosis of chronic myeloid leukemia,” Anal Quant Cytol Histol, vol.33, pp.335-339, 2012.

[7]E. Elveren, and N. Yumuşak. “Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm,” International Journal of Medical Systems, vol.35, pp.329-332, 2011.

[8]O. Er, F. Temurtas, and A. Tanrıkulu. “Tuberculosis disease diagnosis using artificial neural networks,” International Journal of Medical Systems, vol.34, pp.299-302, 2008.

[9]P.S. Sajja, and D.M. Shah. “Knowledge based diagnosis of abdomen pain using fuzzy prolog rules,” Journal of Emerging Trends in Computing and Information Sciences, vol.1(2), pp.55-60, 2010.

[10]M. Swan. “Emerging patient-driven health care models: An examination of health social networks, consumer personalized medicine and quantified self-tracking,” International Journal of Environmental Research and Public Health, vol.6(2), pp.492-525, 2009.

[11]J.H. Frost, and M.P. Massagli. “Social uses of personal health information within patients like me, an online patient community: What can happen when patients have access to one another’s data,” Journal of Medical Internet Research, vol.10(3), e15, 2008.

[12]M.E. Hernando, G. Garcia, E.J. Gomez, and F.D. Pozo. “Intelligent alarms integrated in a multi-agent architecture for diabetes management,” Transactions of the Institute of Measurement and Control, vol.26(3), pp.185-200, 2004.

[13]F. Giampiero, B. Gianluca, C. Alessandro, M. Andrea, and M. Francesco. “A novel method to value real options in health care: The case of a multicohort human papillomavirus vaccination strategy,” Clinical Therapeutics, vol.35(7), pp.904-914, 2013.

[14]M. Mahfouf, M.F. Abbod, and D.A. Linkens. “A survey of fuzzy logic monitoring and control utilisation in medicine,” Artificial Intelligence in Medicine, vol.21(1-3), pp.27-42, 2001.

[15]E. Massimo, F. Ivanoe, and P. Giuseppe. “An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease,” International Journal of Medical Informatics, vol.80(12), pp.e245–e254, 2011.

[16]M. Genesereth, and R. Fikes. “Knowledge interchange format,” Version 3.0 Reference Manual, Technical Report Logic 92-1, Computer Science Department, Stanford University, 1992.

[17]T. Finin, Y. Labrou, and J. Mayfield. “KQML as an agent communication language,” in Software agents, J. M. Bradshaw, Ed. CA: AAAI Press, 1997, pp. 291-316.

[18]J.A. Trivedi, and P.S. Sajja. “Framework for automatic development of type-2 fuzzy, neuro and neuro-fuzzy systems,” International Journal of Advanced Computer Science and Applications, vol.2(1), pp.131-137, 2011.

[19]P.S. Sajja. “Multiagent knowledge-based system accessing distributed resources on knowledge grid,” in Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains, A.V. Senthilkumar, Ed. Hershey, PA: IGI Global Book Publishing, 2010, pp.244-265.

[20]P. S. Sajja. “Research Directions in New Artificial Intelligence: A Case of Neuro-fuzzy System for Web Mining” in Prajna: Journal of Pure and Applied Science, vol. 21, 2013.