Post-survey reflections and issues

Some of the factors responsible for the failure and non-acceptability of earlier systems by the medical doctors and other users can be summarized as follow:

a) Lack of power of explanation b) Lack of transparency.

c) Use of non-probability based decision approach d) Inefficient handling of complex medical situations.

e) Inconsistencies in dealing with uncertain and fuzzy knowledge.

To design an expert system that can surmount above challenges demands more than mere deployment of Frame-based or Rule-based methodology. As noted by Wise(1986), one of the major challenges facing the development of medical expert systems is the overwhelming complexities of knowledge representation and reasoning under uncertainty. In fact, the complexity of patho-physiology typically overwhelms the abilities of doctors to understand all the intricate and relevant details about the excruciating predicament of their patients. Unfortunately, this very fact is inevitable in medical diagnosis and had contributed to the failures of previous attempts in developing reliable expert systems which copiously relied on production rule and non-probabilistic scoring schemes. Researchers have therefore found that it is crucial to represent and reason with uncertain knowledge and that the capture and manipulation of uncertain knowledge is fundamentally different from the corresponding tasks for knowledge held with certainty.

36 2.9.4 Systems that reason under uncertainty

Jimison(1990) emphasized that the goals of modern researchers are to develop various techniques for reasoning under uncertainty and also to develop new approaches to handle complex relationships that exist among evidence and hypothesis. He further stated that understanding and management of numerous problems and challenges that face modern man cannot be explained or solved with accurate certainty. There are sheds of uncertainties that trail most human activities and medicine is not left out of this loop. Attempts have been made by various researchers in developing systems that reason under uncertainty as will be discussed below.

2.9.4.1 The Pathfinder System

One of the earlier attempts in developing a system that employed reasoning under uncertainty was the design of PATHFINDER. Heckerman et al.(1990), stated that PATHFINDER expert system was developed by group of scientists at Stanford University. PATHFINDER concentrated on decision-theoritic methods and uses Bayesian network approach referred to as normative for diagnosis. This decision theory includes probability theory and the maximum-expected-utility principle that provides a set of desirable rules that people believe they should follow or wish to follow when confronted with confusing high stake decisions. This normative methodology adopted by the PATHFINDER researchers may have been abandoned by previous researchers because of difficulties imposed by such technique.

Development of normative expert systems involves complexity of traditional representations of knowledge in a decision theoretic framework. Heckerman (1989) had noted that this complexity has dampened the interest of researchers in applying decision theory in computer-based reasoning systems.

PATHFINDER expert system, therefore, deploys tractable reasoning strategies, human-oriented classification hierarchies and refinement of normative knowledge base.

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Murphy (1966), however noted that PATHFINDER uses hypothetico-deductive reasoning to make inferences about disease condition. Practically, the system reasons about approximately 60 malignant and benign diseases of lymph nodes, constructing differential diagnosis through the consideration of evidence about the status of over 100 morphologic and non-morphologic features visible in lymph-node tissue. The features are each structured into a set of two to ten mutually exclusive and exhaustive values which typically represent the degree of severity of a particular feature. In its operation mode, PATHFINDER system allows a user to enter values for one or more salient features of a lymph node section. Consequently, the system displays a differential diagnosis ordered by likelihood of disease conditions. In response to a query from the user, PATHFINDER recommends a set of features that are the most cost effective for narrowing the differential diagnosis. Further questions or tests may be suggested by the system. This process is iterated and terminates when the differential diagnosis is a single disease or there are no additional tests or questions, or a pathologist determines the informational benefits are not worth the costs of further observations or tests.

Nathwani et al.(1997) evaluated the efficacy and reliability of PATHFINDER expert system by using 30 stained slides from 30 lymph node biopsy specimens on which a consensus diagnosis was made by two group of experts. 10 pathologists in one group used PATHFINDER after a period of training while 9 pathologists used the routine method (diagnosis without computer) to determine a differential diagnosis for 15 slides. The group of experts was later swapped and the process repeated. The results revealed a greater diagnostic accuracy, consistence and reliability when PATHFINDER was used (40%) than when routine method of diagnosis was used (32%). In his conclusion, Nathwani noted that PATHFINDER medical expert system is a valuable tool that assists pathologists in making accurate diagnosis because it has superior attributes than human pathologists to integrate information and to screen for observations incompatible with any specific disease

38 2.9.4.2 The Dxplain System

Further effort to improve the transparency and power of explanation of expert systems in a hybrid system was the development of Dxplain as a decision support system (DSS). Dxplain is a DDX (Differential Diagnosis) expert system developed at the Laboratory of Computer Science at the Massachusetts General Hospital. By using Bayesian Network methodology, DXplain uses a set of clinical findings (signs, symptoms, and laboratory data) to produce a ranked list of diagnosis. The knowledge base of DXplain has over 2,200 diseases and 5,000 symptoms. DXplain is designed to suggest a set of diseases that are associated with a set of clinical findings entered by a health student or practitioner thereby making it a simple tool in the hands of non-medical experts. Hoffer et al.(2005), noted that unlike the previous ES considered, DXplain has survived several years as its developers were able to constantly update its functions and capabilities for the past two decades to keep them in tandem with modern programming concepts. This new concepts include the deployment of DXplain as internet-based platform (WWW interface) (London, 1998). Its applicability over the internet was possible due to its Problem-Learning based features and Dual Protocol Access Route (Barnet et al., 1998).()

Furthermore, Davidzon et al.(2008) described the integration of DXplain with a diagnostic tests sensitivity and specificity and patient demographic data to provide patient-specific positive and negative predictive values at the point of care in a semantic web framework environment. This integration as earlier noted by Elhanan et al.(1996) could be extended as a multiple computer-based medical resources in which the user is neither required to manage the choice of resource and terms, nor use a specialized programming module. However, Cimino et al.(1991), describes a working prototype, the Interactive Query Workstation (IQW) which would allow users to query multiple resources: a medical knowledge base (DXplain), a clinical database (COSTAR/MQL), a bibliographic database (MEDLINE), a cancer database (PDQ), and a drug interaction database (PDR). Another valuable feature of DXplain was its

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ability to provide users with an explanation for why each disease displayed should be considered in the differential diagnosis and in this feature lies its power of explanation, transparency and off course its name. Dxplain could therefore suggest what further clinical information would be useful to collect for each disease condition. Due to the various attractive features exhibited by DXplain, it has found wider acceptability and applicability among various users. But, in a related study, Elkin et al.(2010) explained that the introduction of DXplain into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs), as Bauer et al. (2002) also observed in his study that there is high satisfaction rate among the resident doctors in internal medicine in the teaching hospitals that use this system.

In another study, Bond Et al. (2012) compared DXPlain with other DDXs using the following comparison-criteria : Input method; mobile access; filtering and refinement; lab values, medications, and geography as diagnostic factors; evidence based medicine (EBM) content; references; and drug information content source. The Application of these criteria as well as performance testing supports the use of DxPlain over the other currently available DDX generators. In a nutshell, DXplain is relatively self-explanatory system, requiring little or no end-user training. It can easily be adopted by medical libraries offering, or planning to offer their users access to Web-based materials and resources as they may find this system a valuable addition to their electronic collections.

In the literature review considered so far, most of the expert systems were developed for general medicine. Such earlier expert systems were focused on internal medicine, hence leading to exaggerated discussions on MYCIN, ONCOCIN, PATHFINDER, INTERNIST, Dxplain, etc. The partial concentration on internal medicine by earlier researchers may be justified by the following reasons:

Firstly, internal medicine is the root as well as the bedrock knowledge for all physicians. Secondly, eye specialty areas such as ophthalmology and optometry belong to the class of emerging new fields in

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medicine and are plagued with inadequacy of expertise for development of their knowledge base.

Thirdly, the knowledge base in emerging medical fields is volatile and unstable and could make it less attractive to early researchers.