"The DENDRAL computer program uses established rules of molecular fragmentation to help chemists solve complex structural problems from mass spectral data. This paper describes a computer program called Meta-DENDRAL, that can aid in the discovery of such rules from empirical data on known components. The program uses heuristic methods to search for common structural environments around those bonds that are found to fragment and abstracts plausible fragmentation rules. The program has been tested on the well-characterized, low-resolution mass spectra of aliphatic amines and the high-resolution mass spectra of estrogenic steroids. The program has also discovered new fragmentation rules for mono-, di-, and triketoandrostanes."Journal of the American Chemical Society 98:6168-6178

Gruber T and Cohen P 1989, The design of an automated assistant for acquiring strategic knowledge, ACM SIGART Bulletin:108, (147-151), Online publication date: 1-Apr-1989 .

This text is a description of a computer-based system designed to assist physicians with clinical decision-making. This system, termed MYCIN, utilizes computer techniques derived principally from the subfield of computer science known as artificial intelligence (AI). MYCIN's task is to assist with the decisions involved in the selection of appropriate therapy for patients with infections.

MYCIN contains considerable medical expertise and is also a novel application of computing technology. Thus, this text is addressed both to members of the medical community, who may have limited computer science backgrounds, and to computer scientists with limited knowledge of medical computing and clinical medicine. Some sections of the text may be of greater interest to one community than to the other. A guide to the text follows so that you may select those portions most pertinent to your particular interests and background.

The complete book in a single file.

Computer-Based Medical Consultations: MYCIN focuses on MYCIN, a novel computer-based expert system designed to assist physicians with clinical decisions concerning the selection of appropriate therapy for patients with infections. It discusses medical computing, artificial intelligence, and the clinical problem areas for which the MYCIN program is designed, and it describes in detail how the MYCIN program helps physicians in making decisions. Comprised of seven chapters, this volume begins with an overview of MYCIN and the criteria used in its design. The book also explores MYCIN'S ability to answer questions with respect to its knowledge base and the details of a specific consultation, evaluation and future extensions of the MYCIN system, the limitations and accomplishments of MYCIN, and its contributions in artificial intelligence and computer-based medical decision making. This book is a valuable source of information for computer scientists and members of the medical community.

The first section discusses the importance of having systems that understand the concept of knowledge, and how knowledge is related to action. Section 2 points out some of the special problems that are involved in reasoning about knowledge, and section S presents a logic of knowledge based on the idea of possible worlds. Section 4 integrates this with a logic of actions and gives an example of reasoning in the combined system. Section 5 makes some concluding comments.

This report describes progress in the development of an interactive computer program, termed MYCIN, that uses the clinical decision criteria of experts to advise physicans who request advice regarding selection of appropriate antimicrobial therapy for hospital patients with bacterial infections. Since patients with infectious diseases often require therapy before complete information about the organism becomes available, infectious disease experts have identified clinical and historical criteria that aid in the early selection of antimicrobial therapy. MYCIN gives advice in this area by means of three subprograms: (1) A Consultation System that uses information provided by the physician, together with its own knowledge base, to choose an appropriate drug or combination of drugs; (2) An Explanation System that understands simple English questions and answers them in order to justify its decisions or instruct the user; and (3) A Rule Acquisition System that acquires decision criteria during interactions with an expert and codes them for use during future consultation sessions. A variety of human engineering capabilities have been included to heighten the program's acceptability to the physicians who will use it. Early experience indicates that a sample knowledge base of 200 decision criteria can be used by MYCIN to give appropriate advice for many patients with bacteremia.

Reprinted in Readings in Uncertain Reasoning, G. Shafer and J. Pearl, eds., pp. 259-273, San Mateo, CA: Morgan Kaufmann Publishers, Inc., 1990.See also: Stanford Center for Biomedical Informatics Research (BMIR).… quantifying confirmation and then manipulating the numbers as though they were probabilities quickly leads to apparent inconsistencies or paradoxes. Carl Hempel presented an early analysis of confirmation (Hempel, 1965), pointing out as we have that C[h,e] is a very different concept from P(hle ). His famous Paradox of the Ravens was presented early in his discussion of the logic of confirmation. Let hl be the statement that "all ravens are black" and h2 the statement that "all nonblack things are nonravens." Clearly hi is logically equivalent to h,2. If one were to draw an analogy with conditional probability, it might at first seem valid, therefore, to assert that C[hl,e] = C[h2,e] for all e. However, it appears counterintuitive to state that the observation of a green vase supports hi, even though the observation does seem to support h,2. C[h,e] is therefore different from P(hle) for it seems somehow wrong that an observation of a vase could logically support an assertion about ravens. Another characteristic of a quantitative approach to confirmation that distinguishes the concept from probability was well-recognized by Carnap (1950) and discussed by Barker (1957) and Harrd (1970). They note it is counterintuitive to suggest that the confirmation of the negation of a hypothesis is equal to one minus the confirmation of the hypothesis, i.e., C[h,e] is not 1 - C[-qh,e]. The streptococcal decision rule asserted that a gram-positive coccus growing in chains is a Streptococcus with a measure of support specified as 7 out of 10. This translates to C[h,e]=0.7 where h is "the organism is a Streptococcus" and e is the information that "the organism is a gram-positive coccus growing in chains." As discussed above, an expert does not necessarily believe that C[mh,e] = 0.3. The evidence is said to be supportive of the contention that the organism is a Streptococcus and can therefore hardly also support the contention that the organism is not a Streptococcus. Ch.13 of Mycin Book; revised from Math. Biosci. 23:351-379

An antimicrobial therapy consultation system has been developed which utilizes a flexible representation of knowledge. An ability to display reasons for making decisions at the request of the user permits the program to serve a tutorial as well as consultative role. The feasibility of the judgmental rule approach which the program uses has been demonstrated with a limited knowledge base of approximately 100 rules. Its ultimate success as a clinically useful tool depends upon acquisition of additional rules and thus upon co-operation of infectious disease experts willing to improve the program's knowledge base. The techniques for acquisition, representation, and utilization of knowledge, plus considerations of natural language processing, draw upon and contribute to current Artificial Intelligence research.

"Heuristic DENDRAL is a computer program written to solve problems of inductive inference in organic chemistry. This paper will use the design of Heuristic DENDRAL and its performance on different problems for a discussion of the following topics: 1. the design for generality; 2. the performance problems attendant upon too much generality; 3. the coupling of expertise to the general problem solving processes; 4. the symbiotic relationship between generality and expertness, and the implications of this symbiosis for the study and design of problem solving systems. We conclude the paper with a view of the design for a general problem solver that is a variant of the "big switch" theory of generality."See also: Stanford University Report (ACM Citation)In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 6, pp. 165–190. Edinburgh University Press

"A computer program has been written which can formulate hypotheses from a given set of scientific data. The data consist of the mass spectrum and the empirical formula of an organic chemical compound. The hypotheses which are produced describe molecular structures which are plausible explanations of the data. The hypotheses are generated systematically within the program's theory of chemical stability and within limiting constraints which are inferred from the data by heuristic rules. The program excludes hypotheses inconsistent with the data and lists its candidate explanatory hypotheses in order of decreasing plausibility. The computer program is heuristic in that it searches for plausible hypotheses in a small subset of the total hypothesis space according to heuristic rules learned from chemists."In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 209-254. Edinburgh University Press