Health Care Informatics Expert Systems Expert Systems

Health Care Informatics Expert Systems Expert systems are always computer applications that tend to combine computer equipment, special information and software so that they imitate reasoning and advice of expert human. Being a part of artificial intelligence they offer discipline-specific advice as well as explanation to their users. Artificial intelligence covers a broad field of several aspects of computer generated thought, on the other hand, expert systems focuses narrowly.

The area where expert systems will function well is with specific problems or activities as well as a discrete database of digitized rules, facts, models and cases. Based on the software program, it always integrates a searching and sorting program with a knowledge database. This specific searching plus sorting program for an expert system is called the inferent engine. What makes inferent engine are the entire systematic processing rules and logic that are associated with task at hand. Mathematical probability serves as the core for most of the expert systems.

The other component which is the knowledge database help in storing essential factual, procedural, as well as experiential information about expert knowledge. Using a procedure of knowledge transfer, expertise or the skills and knowledge that sustains a much better as compared to average performance, passes from human expert to knowledge engineer, (Liebowitz Jay, 1998). The work of the knowledge engineer then becomes creating and structuring the knowledge database through completing some of the logical, physical and psychosocial tasks. And this is why expert systems are always called knowledge-based information systems.

The different between expert systems and Decision Support System (DSS) do exist.DSS as a successor of Management Information Systems (MIS), have been traditionally following the decision logic line of thinking and incorporate MIS algorithmic tools in improving the choice activity of decision makers. This can be mathematical programming, optimization methods, and multi-criteria models among others. They tend to be structured related as they assume that decision problem is able to be formulated mathematically and do not stress information processing and display, (Turban, Efraim & Jay E. Aronson,1998).

This is contrary to expert system, being successors of the general problem solver; it follows more on the process paradigm of cognitive decision theory. They don't necessarily assume that the decision problem is able to be formulated in form of mathematical models, they substitute human expertise with the missing efficient algorithms and they do not have structure but they are context related, having much smaller domains of application as compared to decision support system,( Jackson Peter, 1998).

Knowledge is signified in several diverse ways, for example rules, semantic nets, frames, among others. Knowledge is always processed within the inferent machines that work by performing symbol processing (truth values of antecedents, conclusions and others). It wasn't long before the border between DSS and ES became fuzzy. A section of the experts take that ES is part of DSS, another group of experts consider ES and DSS to basically different system while some of them combine these two approaches into knowledge-based ES and DSS.

Despite every system being capable of performing decision in one way or the other, there are still differences that do exist. There are various examples of expert systems that are being used in the hospital settings. Some of the examples include: Mycin; it was a rule-based expert system invented to diagnose and recommend treatment for some given blood infections such as antimicrobial selection for patients with bacteremia or meningitis. Later it was extended to be used in other infectious diseases.

Clinical knowledge in Mycin has its representation in form of IF-THEN rules having particular factors attached to diagnoses. Being a goal directed system through the use of backward chaining reasoning strategy, it lead to exhaustive depth-first search for a proposed solution, CASNET/Glaucoma; It is a general tool used in building expert system for the diagnosis and treatment of diseases. The highly significant expert system application based on CASNET became CASNET/Glaucoma which was used in diagnosing and treating of glaucoma, (Shortliffe E. 1986).

Its expert clinical knowledge came to be represented in a causal-associational network (CASNET) model for describing disease processes. Oncocin; was designed so that it helps physicians in treating patients who are suffering from cancer and receives chemotherapy. It was one of the first DSS which tried to model decisions as well as sequencing actions over a time, by use of customized flowchart language. It provided an extension to skeletal-planning technique to an area of application where.