There is a strong need for cost-effective technologies to manage disease processes and thus reduce morbidity and mortality in the developing countries. Yet bringing intelligent healthcare informatics to bear on the dual problems of reducing healthcare costs and improving quality and outcomes is a challenge even in countries with a reasonably developed technology infrastructure. This paper focused at how appropriate an ap-plication of Medical Diagnosis Expert System (MDES) is to manage diseases in developing countries. MDES is usually designed to enable clinicians to identify diseases and describe methods of treatment to be carried out taking into account the user capability. The MDES described here is implemented using the C Language Integrated Production System (CLIPS). The CLIPS is an expert system, which has a shell composed of four modules: the user interface, the explanation system, the inference engine and the knowledge base editor. In the system, a number of patient cases will be selected as prototypes and stored in a separate database. The knowledge is acquired from literature review, human experts and the internet of the specific domain and is used as a base for analysis, diagnosis and recommendations.
Chandra, R. (2017) Social, Health, and Environmental Infrastructures for Economic Growth. IGI Global, Hershey, PA.
[4]
Viraj, P., et al. (2014) Chemical and Biochemical History of Antimalarials: A Podium of Plasmodium Unicellular Unit. British Biomedical Bulletin, 2, 192-208.
[5]
Rees, P.J., Pattison, J. and Kosky, C. (2014) 100 Cases in Clinical Medicine. 3rd Edition, Taylor & Francis Group, LLC, Boca Raton.
[6]
Harpreet, K., et al. (2012) Artificial Intelligence: Bringing Expert Knowledge to Computers. Discovery, 2, 4-7.
[7]
Bonn (1986) Deutsche Gesellschaft fuer Luft- und Raumfahrt. 443-448.
[8]
Tatro, E. (2004) Artificial Intelligence, Robotics.
[9]
Russell, S.J. and Norvig, P. (2016) Artificial Intelligence A Modern Approach. Pearson Education, Ltd., London.
[10]
Helsgaun, K. (2013) Ten Project Proposals in Artificial Intelligence. Roskilde University, Denmark. Durkin J. (1990) Application of Expert Systems in the Sciences, Ohio. Journal of Science, 90, 171-179.
[11]
Roco, M.C., Bainbridge, W.S., Tonn, B. and Whitesides, G. (2013) Convergence of Knowledge, Technology and Society: Beyond Convergence of Nano-Bio-Info-Cognitive Technologists. Springer International Publishing, Switzerland. https://doi.org/10.1007/978-3-319-02204-8
[12]
Galliers, R.D. and Leidner, D.E. (2009) Strategic Information Management: Challenges and Strategies in Managing Information Systems. 4th Edition, Routledge, London.
[13]
Kendal, S.L. and Creen, M. (2007) An Introduction to Knowledge Engineering. Springer, London.
[14]
Constantinou, A., Fenton, N. and Martin, M. (2012) pi-Football: A Bayesian Network Model for Forecasting Association Football Match Outcomes. Knowledge-Based Systems, 36, 322-339. https://doi.org/10.1016/j.knosys.2012.07.008
[15]
Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E. and Shepherd, K. (2016) A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study. Expert Systems with Applications, 60, 141-155. https://doi.org/10.1016/j.eswa.2016.05.005
[16]
Constantinou, A., Freestone, M. and Marsh, W. (2015) Causal Inference for Violence Risk Management and Decision Support in Forensic Psychiatry. Decision Support Systems, 80, 42-55. https://doi.org/10.1016/j.dss.2015.09.006
[17]
Shortliffe, E.H. (1976) Computer-Based Medical Consultation. MYCIN. Elsevier, New York.
