To be presented at the Eighteenth Annual Meeting of the Drug Information Association, Kansas City, Missouri, June 16, 1982. Edward H. Shortliffe, MD, PhD Assistant Professor of Medicine and Computer Science Heuristic Programming Project Department of Medicine Stanford University School of Medicine Stanford, California 94305 To be presented at the Eighteenth Annual Meeting of the Drug Information Association Kansas City, Missouri 16 June 1982 ABSTRACT The trend towards increased use of computer-based symbolic reasoning techniques for clinical decision making programs stems from the dual goals of improving the performance and increasing the acceptance of such systems. This talk will summarize the design considerations that have encouraged some recent investigators to turn to artificial intelligence techniques when'bJllding consultation systems. Some of the recent experimental consultation systems are less concerned with reaching correct diagnoses than with advising physicians un optimal treatment strategies for patients with known serious ..',Iscases. Examples for discussion will be drawn from (1) the MYCIN system, a consultation program to advise physicians on the selection of antimicrobials for patients with bacteremia or meningitis, and (2) ONCOCIN, a recently developed program for advising oncologists on therapy adjustment in the manarement of patients enrolled in cancer chemotherapy protocols.

This memo contains two papers that deal with medical computing. The first, written for a book on cybernetics and society, examines the range of medical computing systems, plus some of the logistical and human engineering challenges limiting their utility or acceptance. It addresses five recurring themes that characterize the introduction of medical computing systems: 1) the need for the proposed application, 2) the system users, 3) the logistics of system introduction, 4) the required computational techniques, and 5) the required technological resources. In the context of these topics, suggestions are offered for long-range research and resource policies that are appropriate for assuring the development of practical clinical computing. The second paper, presented at a meeting on artificial intelligence in May 1980, takes a more detailed look at the reasons that medical computing systems have had a limited impact on clinical medicine. When one examines the most common reasons for poor acceptance of such systems, the potential relevance of artificial intelligence techniques becomes evident. The paper proposes design criteria for clinical computing systems and demonstrates their relationship to current research in knowledge engineering. The MYCIN System is used to illustrate the ways in which one research group has responded to the design criteria cited.

This paper briefly outlines the difference between Branching on the bas of response was the first step coward traditional instructional programs and MAI.

The language designer typically designs that language with one particular application domain in mind: as subsequent types of applications are tned, what had originally been useful features are found to be undesirable limitations, and the language is overhauled or scrapped. One remedy to this bleak cycle might be to construct a representation language whose domain is the field of representational languages itself. One remedy to this bleak cycle might be to construct a representation language whose domain is th field of representation languages itself, a system which could then be tailored to suit many specific applications. Toward this end, we (Professor Douglas Lena and 1) have designed and implemented RLL-1, an object-centered2 Representation Languange Language.3 A representation language language (r11) must explicitly represent the components of representation languages in general and of itself in particular.

Section 1 1 Introduction The MOLGEN project has focused on research Into the applications of symbolic computation and Inference -to the field of molecular biology. This has taken the specific form of systems which provide assistance to the experimental scientist in various tasks, the most important of which have been the design of complex experiment plans and the analysis of nucleic acid sequences. During the period of further research proposed in this document, we plan to expand and improve these systems and build new ones to meet the rapidly growing needs of the domain of recombinant DNA technology. We do this with the view of including.

MEAN ARTERIAL PRESSURE is ACCEPTABLE (currently one hour).

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Overview There are two main area.

Each of Since the early 1970's, researchers on computei-- these programs uses a representation scheme, based medical reasoning have begun to recognize known as production rules [3], to encode the the potential benefits of applying symbolic reasoning medical knowledge used for decision making.

Those of us involved In the creation of the Handbook of Artificial Intelligence, both writers and editors, have attempted to make the concepts, methods, tools, and main results of artificial Intelligence research accessible to a broad scientific and engineering audience. Currently, Al work Is familiar mainly to its practicing specialists and other interested computer scientists. Yet the field Is of growing interdisciplinary interest and practical Importance. With this book we are trying to build bridges that are easily crossed by engineers, scientists in other fields, and our own computer science colleagues. In the Handbook we Intend to cover the breadth and depth of Al, presenting general overviews of the scientific issues, as well as detailed discussions of particular to -hniques and Important Al systems.

We have developed and partially Imp;zmented an "automated consultant" called SACON (Structural Analysis CONsultant), using the EMYCIN system as Its framework. SACON advises non expert engineers in the use of a large, general-purpose structural analysis program. The structure of the knowledge b,:se, including the major concepts used and Inferences drawn by the consultant, is presented. We conclude by making some observations 11 light of this application about the EMYCIN system as a representational vehicle and the process of acquiring knowledge for rule-based systems. Key words: knowledge-based systems, knowledge acquisition, knowledge representation, automated consultant, structural analysis, inference structure. This research was supported by the Defense Advanced Research Projects Agency (ARPA Order No. 2494 Contract No. DAHC15-73-C-0435) and the Air Force Flight Dynamics Laboratory. Reprinted from the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, August 1979. Used by permission of the International Joint Conference on Artificial Intelligence, Inc.; copies of the Proceedings are available from Morgan Kaufmann Publishers, Inc., 95 First Street, Los Altos, CA 94022, USA.