It is important to cover for the following probable infection(s) and associated organism(s): INFECTION-1 is BACTEREMIA ITEM-l E.COLI [ORGANISM-I] ITEM-2 KLEBSIELLA [ORGANISM-I] ITEM-3 ENTEROBACTER [ORGANISM-I] ITEM-4 KLEBSIELLA-PNEUMONIAE [ORGANISM-I]

There is little doubt that the decision to use rules to encode infectious disease knowledge in the nascent MYCIN system was largely influenced by our experience using similar techniques in DENDRAL. However, as mentioned in Chapter 1, we did experiment with a semantic network representation before turning to the production rule model. The impressive published examples of Carbonell's SCHOLAR system (Carbonell, 1970a; 1970b), with its ability to carry on a mixed-initiative dialogue regarding the geography of South America, seemed to us a useful model of the kind of rich interactive environment that would be needed for a system to advise physicians. Our disenchantment with a pure semantic network representation of the domain knowledge arose for several reasons as we began to work with Cohen and Axline, our collaborating experts. First, the knowledge of infectious disease therapy selection was ill-structured and, we found, difficult to represent using labeled arcs between nodes. Unlike South American geography, our domain did not have a clear-cut hierarchical organization, and we found it challenging to transfer a page or two from a medical textbook into a network of sufficient richness for our purposes. Of particular importance was our need for a strong inferential mechanism that would allow our system to reason about complex relationships among diverse concepts; there was no precedent for inferences on a semantic net that went beyond the direct, labeled relationships between nodes.1 Perhaps the greatest problem with a network representation, and the greatest appeal of production rules, was our gradually recognized need to deal with small chunks of domain knowledge in interacting with our expert collaborators.

Since production systems (PS's) were first proposed by Post (1943) general computational mechanism, the methodology has seen a great deal of development and has been applied to a diverse collection of problems. Despite the wide scope of goals and perspectives demonstrated by the various systems, there appear to be many recurrent themes. We present an analysis and overview of those themes, as well as a conceptual framework by which many of the seemingly disparate efforts can be viewed, both in relation to each other and to other methodologies. Accordingly, we use the term production system in a broad sense and show how most systems that have used the term can be fit into the framework. The comparison to other methodologies is intended to provide a view of PS characteristics in a broader context, with primary reference to procedurally based techniques, but also with reference to more recent developments in programming and the organization of data and knowledge bases.

Artificial Intelligence (AI) is that branch of computer science dealing with symbolic, nonalgorithmic methods of problem solving. Several aspects of this statement are important for understanding MYCIN and the issues discussed in this book. First, most uses of computers over the last 40 years have been in numerical or data-processing applications, but most of a person's knowledge of a subject like medicine is not mathematical or quantitative. It is symbolic knowledge, and it is used in a variety of ways in problem solving. Also, the problem-solving methods themselves are usually not mathematical or data-processing procedures but qualitative reasoning techniques that relate items through judgmental rules, or heuristics, as well as through theoretical laws and definitions.

Artificial intelligence, or AI, is largely an experimental science--at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work.

Addison-Wesley Publishing Company Reading, Massachusetts Menlo Park, California London Amsterdam Don Mills, Ontario Sydney This book is in The Addison-Wesley Series in Artificial Intelligence. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.

R l is a program that configures VAX -11/780 computersystems. Given a customer's order, it determines

It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

Finite chase, or alternatively chase termination, is an important condition to ensure the decidability of existential rule languages. In the past few years, a number of rule languages with finite chase have been studied. In this work, we propose a novel approach for classifying the rule languages with finite chase. Using this approach, a family of decidable rule languages, which extend the existing languages with the finite chase property, are naturally defined. We then study the complexity of these languages. Although all of them are tractable for data complexity, we show that their combined complexity can be arbitrarily high. Furthermore, we prove that all the rule languages with finite chase that extend the weakly acyclic language are of the same expressiveness as the weakly acyclic one, while rule languages with higher combined complexity are in general more succinct than those with lower combined complexity.

The need for health providers to share information is a pressing need in our ever more connected world. A patient's health information should seamlessly flow from labs to hospitals to primary care offices. To address this need, in this paper we present the Health E-Match, which focuses on the matching health terms in support of semantic interoperability. Health E-Match determines the semantic similarity between data items, realizing, for instance, that "BHGC (UR)" and "BETA-HCG (QUAL)" both refer to the same pregnancy test, known as "Beta human chorionic gonadotropin, urine qualitative." Our approach is grounded in probabilistic machine learning, and leverages several sophisticated methods for comparing the similarity between medical data items beyond simple edit distance. We present two large scale, real-world experiments to verify that our approach is both accurate and has the ability to eventually be "universal" in that models trained on one set of data translate to strong performance on data from a completely different provider.