Ehealth advisory expert system for HIV/AIDS patients in South Africa

Abstract

Human immunodeficiency virus (HIV) / Acquired immune deficiency syndrome (AIDS) is a major global health problem. It is the greatest threat to the reconstruction and development of South Africa. Advances in Information and Communication Technology (ICT) have facilitated development of medical expert systems. They have proved their usefulness by providing precise, quick and inexpensive consultation. Advisory expert system is seen as an enhancement tool in providing homebase care to people living with HIV/AIDS. In this paper we discuss our research motives, related work and the design of the web-based advisory expert system that allows HIV patients to manage the HIV-related symptoms.

Implementation constraints and opportunities in developing expert system solutions for diagnosis of common diseases found in Taraba State

“…They said, this system can be developed further with increasing the knowledge rules from one side and with adding the neural network to the system from the other side. [12] Expert system can be developed; after selecting the domain they want to build expert system, you will precede to knowledge acquisition which involves the acquisition of knowledge from human experts, books, or documents [9]. They formally confirmed that knowledge acquisition is the bottleneck in Expert System development today.…”

“…They choose the approach because it involves the domain experts (health care workers) and it allows correction of the system to be made early in the process. [9] In designing and implementing our expert system, we adopted some steps in the diagnosis and treatment of hepatitis B. The steps include: analysis of current system, identifying the problem of the current system, analysis of the proposed system, system design, which is classified under Structured Systems Analysis and Design Methodology (SSADM).…”

A Formal Approach to Designing Reliable Advisory Systems

“…These are: (1) the knowledge base that lists domain specific knowledge; (2) a data monitoring agent that collects (stream) data; (3) the inference engine that interprets problems from the data and uses expert knowledge to deduce suitable solutions and (4) the user interface for supporting human-computer interactions. In the literature, there are many examples of advisory systems that are deployed in various industrial settings using this architecture, such as finance, medicine and process control [3][4][5][6][7][8][9][10]. However, since system failures in these settings can result in potentially serious consequences, such as loss of revenue, loss of productivity and damage to property, it is important to ensure that advisory systems are both reliable and dependable [13].…”

“…The basic purpose of an advisory system is to assist the end-user in identifying suitable solutions to complex, unstructured problems [1][2][3][4][5][6][7][8][9][10]. In decision-making, an un-structured problem is one that is characterised with contextual uncertainty, where there are no definite processes in place for predictably responding to a problem that is, well-defined actions that do not necessarily lead to predictable outcomes [2].…”

“…From these applications, it is clear that designs of advisory systems are based on the illustrated architecture and perform according to one of two main styles. These are: (1) monitoring and evaluation and (2) diagnosis and recovery [2][3][4][5][6][7][8][9]. In the monitoring and evaluation style, advisory systems simply monitor data streams to identify statistical anomalies that may represent a potential problem or to identify predictive behaviour patterns.…”