This is a remarkab'y exciting time to be involved professionally in the field of medical informatics. The underlying scientific principles are beginning to be identified and defined, educators are increasingly acknowledging the importance of thc field for physicians of the present and future, and the tec mology itself is growing at rates that make the future of the field both unbounded and impossible to predict. One has the sense that what was once a field for pioneers is now reaching the stage of established settlements, with a history, traditions, and a feel of permanence. It is therefore appropriate that, at the beginning of ddiberations designed to achieve significant educational goals for the field, we might start by considering the discipline itself and the characteristics that hnve tended to separate it from other traditional academic and research medical specialties. I would like to begin by assuming that certain basic points are well accepted and need not be defended here: first that medical informatics holds both realized and potential importance for the science anc practice of medicine, and second, that there is a need for all medical practitioners to be familiar both with information handling technology and with the underlying principles that make the field relevant, regardless of whether computers are involved.

Simuldtois serve two major purposes: the first is the verification of scientific thP.ories the second is xperimental result prcliction. Thr? verification function is called upon when existing t:leories are btling cxtended or new theories are being ç enerated to explain experimental data: the predictive capabilities are used to predict laboratory results in order to eliminate a great deal of experimmtal effort. An esoecially important role for a simulation pro.warn would be as par; of a larger employing art ficial innelligence techniques to develop mcd.els of a biological system bar:ed on experimental OUSerwitiOris. Such a program would accept as Input observations of a s; stem cud would!fiocii:ce as output a model for tn.t system that could account for the observations. The r.:niu!ation portion of such a program would be a crucial tool for ensuring that on:y theories mat were con::..-4 with thc data were dewloocci. It's a major research goal Cf the HOLDEN pi met to explore methods tor building a systym

It has been argued by Hartley and Sleeman.

This paper reports the results obtained with a group of 24 14-year-old pupils when presented with sets of algebra tasks by the Leeds Modelling System.

The objective of this paper is to present a new method for the computer representation of empirically derived causal relationships (CR's). This method draws on the theory of multivariate linear models and path analysis. The method is contrasted with the predicate calculus methods developed by other Al researchers. The representation presented here has been used to store information on medical CR's derived empirically from a large clinical database by a computer program called RX. The principal emphasis in the representation is on capturing the intensities and variances of effects and the variation in the effects across a patient population. Once incorporated into RX's knowledge base, this information is subsequently used by RX in determining the validity of other CR's. The representation uses a directed graph formalism in which the nodes are frames and the arcs contain seven descriptive features of individual CR's: intensity, distribution, direction, mathematical form, setting, validity, and evidence. Because natural systems (such as the human body) are inherently probabilistic, linear models are useful in representing causal flow in them. Knowledge of natural systems is fundamentally probabilistic because of I) irreducible indeterminism in their component processes, 2) difficulties in accurately measuring all relevant variables, 3) variation among individuals in a population, and 4) inadequate scientific theory.

The time-sharing systems that were used to develop them are largely inadequate due to the burden placed upon computing and storage resources. Consequently, some of this research is being shifted to personal workstations which provide a sophisticated graphics interface in addition to satisfying the requirements for computational speed and memory. This paper examines the use of that graphics interface in the development of a tool for a system builder. Introduction Medical expert systems are consultation programs designed -..o give advice using both formal knowledge and the judgmental expertise of clinical specialists. When these systems first began to appear in the 1970's, many observers doubted their future role because their size and complexity placed an inordinate burdenlin - the processing and storage resources of conventional time-sharing systems.

People's certainty of the past is D follows from A. B. and C. It may be that A. B. and C, limited by the fidelity of the devices that record it, their though certain, suggest but do not confirm D. in which case knowledge of the present is always incomplete, and their the number associated with D might be less than the 1.0 that knowledge of the future is but speculation. Even though usually represents certainty in such systems. If A. B. or C nothing is certain, people behave as if almost nothing is are uncertain, then the number associated with D is modified uncertain. They are adept at discounting uncertainty -- to account for the uncertainty of its premises. These numbers making it go away. This article discusses how Al programs are given different names by different authors; we refer might be made similarly adept.

FOLIO is an expert system to assist portfolio managers. It interviews a client and, on the basis of expert knowledge, determines the client's investment goals and the portfolio that best meets them. For example, FOLIO may determine that one client requires a preponderance of tax-free investments and a substantial hedge against rising short-term interest rates, while another is best served by a mix of low-risk dividend-oriented stocks and intermediate-term bonds. FOLIO is a test bed for a theory of heuristic reasoning about uncertainty (Cohen and Grinberg, 1983), and its task has many parallels to estab!ished Al paradigms such as diagiosis in medicine and construction of a student model in ICAI domains (Barr and Feigenbaum, 1982). FOLIO uses a goal programming algorithm (Hillier and Lieberman, 190) as a relaxation method for resolving the client's multiple goals into a portfolio that fi'.s them optimally.

To appear inProc. of the American Association for Medical Systems & Informatics, 1983 Reprinted by permission of the American Association for Medical Systems and Informatics (AAMSI). Hardware advances in the next decade promise to make poss:*ale new medical educational technologies. New media for expressing, collecting, and sharing knowledge will provide students with means for coping with the increasing amounts of information. Novel means of graphically modelling physical phenomena--providing motivating and intuitively pleasing means for explorative interaction--could complement and sometimes replace traditional text material. Intelligent programs may serve as assistants, serving roles ranging from calculator to librarian to tutor, embracing a full range of secretarial and problen solving aids.

It has been argued that the problem of medical diagnos.r_s is fundamentally ill-structured.