Scientific Subproject Abstracts 5P41-RR0Q785-14

REFERENCES

1. Shortliffe, E.H., Scott, A.C., Bischoff, M.B., Campbell, A.B. van Melle,
W. and Jacobs, C.D.. ONCOCIN: An expert system for oncology protocol

management. Proc. Seventh IJCAI, pp. 876-881, Vancouver, B.C., August,
1981.

2. Duda, R.O. and Shortliffe, E.H: Expert systems research. Science
220:261-268, 1983.

3. Langlotz, C.P. and Shortliffe, E.H.: Adapting a consultation system to
critique user plans. Int. J. Man-Machine Studies 19:479-496, 1983.

4. Bischoff, M.B., Shortliffe, E.H., Scott, A.C., Carlson, R.W. and Jacobs, C.D.:
Integration of a computer-based consultant into the clinical setting.
Proceedings 7th Annual Symposium on Computer Applications in Medical
Care, pp. 149-152, Baltimore, Maryland, October 1983.

5. Hickam, D.H., Shortliffe, E.H., Bischoff, M.B., Scott, A.C., Jacobs, C.D: A
study of the treatment advice of a computer-based cancer chemotherapy

protocol advisor (Memo KSL-85-21). Annals of Internal Medicine 103(6 pt
1):928-936 (1985).

6. Kent; D.L., Shortliffe, E.H., Carlson, R.W., Bischoff, M.B., Jacobs, C.D.:
Improvements in data collection through physician use of a computer-based

chemotherapy treatment consultant (Memo KSL-85-22). Journal of Clinical
Oncology 3:1409-1417, 1985.

7. Tsuji, S. and Shortliffe, E.H.: Graphical access to a medical expert system:

I. Design of a knowledge engineer's interface (Memo KSL-85-11). Meth.
Inf. Med., 25:62-70, 1986.

8. Lane, C.D., Differding, J.C., Shortliffe, E.H.: Graphical access to a medical
expert system: II. Design of an interface for physicians (Memo KSL-85-15).
Meth. Inf, Med., 25:143-150, 1986.

9. Shortliffe, EH. Medical expert systems: Knowledge tools for physicians.

Memo KSL-86-52. Special issue on Medical Informatics, West. J. Med.
145:830-839, 1986.

E. H. Shortliffe 226
5P41-RR00785-14 Scientific Subproject Abstracts

Stanford Project: PROTEAN Project

Principal Investigators: Oleg Jardetzky
(JARDETZK Y@SUMEX-AIM.STANFORD.EDU)
Nuclear Magnetic Resonance Lab, School of Medicine
Stanford University Medical Center
Stanford, California 94305

Bruce G. Buchanan, Ph.D.
(BUCHANAN@SUMEX-AIM.STANFORD.EDU)
Computer Science Department
Stanford University
Stanford, California 94305

Contact Person: Bruce G. Buchanan

The goals of this project are related both to biochemistry and artificial intelligence: (a)
use existing AI methods to aid in the determination of the 3-dimensional structure of
proteins in solution (not from x-ray crystallography proteins), and (b) use protein
structure determination as a test problem for experiments with the AI problem-solving
structure known as the Blackboard Model. Empirical data from nuclear magnetic
resonance (NMR) and other sources may provide enough constraints on structural
descriptions to allow protein chemists to bypass the laborious methods of crystallizing a
protein and using X-ray crystallography to determine its structure. This problem
exhibits considerable complexity, yet there is reason to believe that AI programs can be
written that reason much as experts do to resolve these difficulties. A prototype
knowledge-based system assembles major secondary structures of a protein into families
of structures compatible with a given set of distance constraints under the control of an
explicit assembly strategy. Structures can also be refined at the atomic level of detail
using constraints within secondary structures and between amino acid side chains to
further restrict the 3-dimensional structure found. By generalizing this approach to the
assembly of arrangements of objects subject to constraints, we have developed a
language for specifying actions and control for problem solving in similar problem
domains.

REFERENCES

1. Altman, R. and Jardetzky, O: New strategies for the determination of

macromolecular structures in solution. Journal of Biochemistry (Tokyo),
Vol. 100, No. 6, p. 1403-1423, 1986.

bo

. Altman, R. and Buchanan, B.G.: Partial Compilation of Control Knowledge.
To appear in: Proceedings of the AAAT 1987.

3. Brinkley, J., Cornelius, C., Altman, R., Hayes-Roth, B. Lichtarge, O., Duncan,
B., Buchanan, B.G., Jardetzky, O.: Application of Constraint Satisfaction
Techniques to the Determination of Protein Tertiary Structure. Report
KSL-86-28, Department of Computer Science, 1986.

4. Brinkley, James F., Buchanan, Bruce G., Altman, Russ B., Duncan, Bruce S.,

Cornelius, Craig W.: A Heuristic Refinement Method for Spatial Constraint
Satisfaction Problems. Report KSL 87-95, Department of Computer Science.

227 BE. H. Shortliffe
Scientific Subproject Abstracts

5.

10.

11.

12.

13.

14.

Buchanan, B.G., Hayes-Roth, B., Lichtarge, O., Altman, A., Brinkley, J.,
Hewett, M., Cornelius, C., Duncan, B., Jardetzky, O.:The Heuristic Refinement
Method for Deriving Solution Structures of Proteins. Report KSL-85-41.
October 1985.

. Garvey, Alan, Cornelius, Craig, and Hayes-Roth, Barbara: Computational

Costs versus Benefits of Control Reasoning. Report KSL 87-11, Department
of Computer Science.

. Hayes-Roth, B: The Blackboard Architecture: A General Framework for

Problem Solving? Report HPP-83-30, Department of Computer Science,
Stanford University, 1983.

. Hayes-Roth, B.: BBI: An Environment for Building Blackboard Systems

that Control, Explain, and Learn about their own Behavior. Report
HPP-84-16, Department of Computer Science, Stanford University, 1984.

. Hayes-Roth, B: <A Blackboard Architecture for Control. Artificial

Intelligence 26:251-321, 1985.

Hayes-Roth, B. and Hewett, M.: Learning Control Heuristics in BB/. Report
HPP-85-2, Department of Computer Science, 1985.

Hayes-Roth, B., Buchanan, B.G., Lichtarge, O., Hewett, M., Altman, R.,
Brinkley, J., Cornelius, C., Duncan, B., and Jardetzky, O.: PROTEAN:
Deriving protein structure from constraints. Proceedings of the AAAI, 1986,
p. 904-909.

Jardetzky, O.: A Method for the Definition of the Solution Structure of
Proteins from NMR and Other Physical Measurements: The LAC-Repressor
Headpiece. Proceedings of the International Conference on the Frontiers of
Biochemistry and Molecular Biology, Alma Alta, June 17-24, 1984, October,
1984,

Lichtarge, Olivier: Structure determination of proteins in solution by NMR.
Ph.D. Thesis, Stanford University, November, 1986.

Lichtarge, Olivier, Cornelius, Craig W., Buchanan, Bruce G., Jardetzky, Oleg:
Validation of the First Step of the Heuristic Refinement Method for the
Derivation of Solution Structures of Proteins from NMR Data., April 1987.
Submitted to Proteins: Structure, Function, and Genetics.

E. H. Shortliffe 228

S5P41-RR00785-14
5P41-RRO00785-14 Scientific Subproject Abstracts

Stanford Project: RADIX -- DERIVING KNOWLEDGE FROM
TIME-ORIENTED CLINICAL DATABASES

Principal Investigators: Robert L. Blum, M.D.
Departments of Medicine
and Computer Science
Stanford University
Stanford, California 94305
(415) 497-9421 (BLUM@SUMEX-AIM)

Gio C.M. Wiederhold, Ph.D.

Department of Computer Science

Stanford University

Stanford, California 94305

(415) 497-0685 (WIEDERHOLD@SUMEX-AIM)

The objective of clinical database (DB) systems is to derive medical knowledge from the
stored patient observations. However, the process of reliably deriving causal
relationships has proven to be quite difficult because of the complexity of disease states
and time relationships, strong sources of bias, and problems of missing and outlying
data.

The first goal of the RADIX Project is to explore the usefulness of knowledge-based
computational techniques in solving this problem of accurate knowledge inference from
non-randomized, non-protocol patient records. Central to RADIX is a knowledge base
(KB) of medicine and statistics, organized as a taxonomic tree consisting of frames with
attached data and procedures. The KB is used to retrieve time-intervals of interest
from the DB and to assist with the statistical analysis. Derived knowledge is
incorporated automatically into the KB. The American Rheumatism Association DB
containing records of 1700 patients is used.

The second goal of the project is to develop a program and set of techniques for
automated summarization of patient records. The summarization program is designed to
automatically create patient summaries of arbitrary and appropriate complexity as an
aid for tasks such as clinical decision making, real-time patient monitoring, surveillance
of quality of care, and eventually automated discovery. Two prototype summarization
modules have been implemented in KEE on the Xerox 1108 workstation.

SOFTWARE AVAILABLE ON SUMEX
RADIX--(excluding the knowledge base and clinical database) consists of approximately

400 INTERLISP functions. The following groups of functions may be of interest apart
from the RADIX environment:

SPSS Interface Package -- Functions which create SPSS source decks and read
SPSS listings from within INTERLISP.

Statistical Tests in INTERLISP -- Translations of the Piezer-Pratt
approximations for the T, F, and Chi-square ‘ests into LISP.

Time-Oriented Data Base and Graphics Package -- Autonomous package for
maintaining a time-oriented database and displaying labelled time-intervals.

229 E. H. Shortliffe
Scientific Subproject Abstracts 5P41-RR00785-14

REFERENCES
Monograph

Blum, R.L.: Discovery and representation of causal relationships

from a large time-oriented clinical database: The RX project.

in D.A.B. Lindberg and P.L. Reichertz (Eds.), LECTURE NOTES IN
MEDICAL INFORMATICS, Vol. 19, Springer-Verlag, New York, 1982.

Journal Articles

Blum, R.L.: Computer-Assisted Design of Studies using Routine Clinical
Data: Analyzing the Association of Prednisone and Cholesterol.
Annals of Internal Medicine 104(6):858-868, June, 1986.

Blum, R.L.: Discovery, confirmation, and incorporation of causal
relationships from a large time-oriented clinical database:

The RX Project. Computers and Biomedical Research 15(2):164-187,
April, 1982.

Walker, M.G. How Feasible is Automated Discovery? IEEE Expert
2(1):70-82, Spring, 1987.

Conference Proceedings

DeZegher-Geets, I.M., Freeman, A.G., Walker, M.G., Blum, R.L., and
Wiederhold, G.C.M. Computer-aided Summarization of a Time-oriented
Medical Data Base. In Proceedings of the Third Annual Conference on

Computerization of Medical Records. Institute for Medical Record Economics,
1987.

Downs, S., Walker, M.G., and Blum, R.L. Automated Summarization of
On-line Medical Records. In Proceedings of the Fifth World Congress on
Medical Informatics (Medinfo), pages 800-804. Elsevier Science Publishers,
1986.

Walker, M.G., and Blum, R.L. Towards Automated Discovery from Clinical
Databases: the RADIX Project. In Proceedings of the Fifth World Congress
on Medical Informatics (Medinfo), pages 32-36. Elsevier Science Publishers,
1986.

E. H. Shortliffe 230
5P41-RR00785-14 Scientific Subproject Abstracts.

National AIM Project: CADUCEUS
(INTERNIST-I)

QMR
(Quick Medical Reference)

Principal Investigators:
CADUCEUS PROJECT:
Harry E. Pople, Ph.D. (POPLE@SUMEX-AIM)
Jack D. Myers, M.D. (MYERS@SUMEX-AIM)

QMR PROJECT:

Randolph A. Miller, M.D.(RMILLER@SUMEX-AIM)
Fred E. Masarie, Jr. M.D. (MASARIE@SUMEX-AIM)
Jack D. Myers, M.D. (MYERS@SUMEX-AIM)
University of Pittsburgh

Pittsburgh, Pennsylvania 15261

Dr. Pople: (412) 624-3490
Dr. Myers: (412) 648-9933
Dr. Miller: (412) 648-3190
Dr. Masarie: (412) 648-3190

The major goal of both the CADUCEUS and INTERNIST-I/QMR Projects is to
produce a reliable and adequately complete diagnostic consultative program in the field
of internal medicine. Although this program is intended primarily to aid skilled
internists in complicated medical problems, the program may have spin-offs as a
diagnostic and triage aid to physicians’ assistants, rural health clinics, military medicine
and space travel. In the design of INTERNIST-I and QMR, we have attempted to
model the creative, problem-formulation aspect of the clinical reasoning process. The
program employs a novel heuristic procedure that composes differential diagnoses,
dynamically, on the basis of clinical evidence. During the course of a INTERNIST-1
consultation, it is not uncommon for a number of such conjectured problem foci to be
proposed and investigated, with occasional major shifts taking place in the program’s
conceptualization of the task at hand. QMR is broader in scope than INTERNIST-I or
CADUCEUS, in that it provides quick and efficient access to the INTERNIST-I/QMR
knowledge base to provide low and intermediate level informational support for
physicians’ decision-making, in addition to providing consultative advice.

SOFTWARE AVAILABLE ON SUMEX

Versions of INTERNIST-I are available for experimental use, but the project continues
to be oriented primarily towards research and development; hence, a stable production
version of the system is not yet available for general use. QMR has been shared on a
restricted basis with a limited number of academic colleagues, who have agreed to give
the QMR development team feedback on the program's strengths and weaknesses.

231 E. H. Shortliffe
Scientific Subproject Abstracts 5P41-RRO0785-14

National AIM Project: CLIPR -- HIERARCHICAL MODELS
OF HUMAN COGNITION

Principal Investigators: Walter Kintsch, Ph.D. (KINTSCH@SUMEX-AIM)
Peter G. Polson, Ph.D. (POLSON@SUMEX-AIM)
Computer Laboratory for Instruction
in Psychological Research (CLIPR)
Campus Box 345
Department of Psychology
University of Colorado
Boulder, Colorado 80309
(303) 492-6991
Contact: Dr. Peter G. Polson (Polson@SUMEX-AIM)

The CLIPR Project is concerned with the modeling of complex psychological processes.
It is comprised of two research groups. The prose comprehension group has completed
a project that carries out the text analysis described by van Dijk & Kintsch (1983),
yielding predictions of the recall and readability of that text by human subjects. The
human-computer interaction group is developing a quantitative theory of that predicts
learning, transfer, and performance for a wide range of computer-tasks, e.g. text editing,
Kieras & Polson (1985).

SOFTWARE AVAILABLE ON SUMEX

A set of programs has been developed to perform the microstructure text analysis
described in van Dijk & Kintsch (1983) and Kintsch & Greeno (1985). The program
accepts a propositionalized text as input, and produces indices that can be used to
estimate the text’s recall and readability.

REFERENCES

l. Fletcher, R.C.: Understanding and solving word arithmetic problems: A
computer simulation. Technical Report No. 135, Institute of Cognitive
Science, Colorado, 1984.

2. Kieras, D.E. and Polson, P.G.: The formal analysis of user complexity. Int.
J. Man-Machine Studies, 22, 365-394, 1985.

2d

. Kintsch, W. and van Dijk, T.A.: Toward a model of text comprehension and
production. Psychological Rev. 85:363-394, 1978.

4. Kintsch, W. and Greeno, J.G.: Understanding and solving word arithmetic
problems. Psychological Review, 1985, 92, 109-129.

5. Polson, P.G. and Kieras, D.E.: 4 formal description of users’ knowledge of
how to operate a device and user complexity. Behavior Research Methods,
Instrumentation, & Computers, 1984, 16, 249-255,

6. Polson, P.G. and Kieras, D.E.: A quantitative model of the learning and
performance of text editing knowledge. In Borman, L. and Curtis, B. (Eds.)
Proceedings of the CHI 1985 Conference on Human Factors in Computing.
New York: Association for Computing Machinery. pp. 207-212, 1985.

E. H. Shortliffe

i
tua
i
5P41-RR00785-14

7.

10.

il,

Polson, P.G. and Jeffries, R.: /nstruction in general problem solving skills:
An analysis of four approaches. In (Eds.) Siegel, J., Chipman, S., and Glaser,
R. Thinking and learning skills: Relating instructions to basic research:
Vol.l. Hillsdale, N.J.: Lawrence Erlbaum Associates, pp. 414-455.

. Polson, P.G., Muncher, E., and Engelbeck, G: Test of a common elements

theory of transfer. In Mantei, M. and Orbeton, P. (Eds.) Proceedings of the
CHI 1986 Conference on Human Factors in Computing. New York:
Association for Computing Machinery. pp. 78-83, 1986.

.van Dijk, T.A. and = Kintsch, W.: STRATEGIES OF DISCOURSE

COMPREHENSION. Academic Press, New York, 1983.

Young, S.: A theory and simulation of macrostructure. Technical Report No.
134, Institute of Cognitive Science, Colorado, 1984.

Walker, H.W. & Kintsch, W.: Automatic and strategic aspects of knowledge
retrieval. Cognitive Science, 1985, 9, 261-283.

233 E. H. Shortliffe

Scientific Subproject Abstracts
Scientific Subproject Abstracts 5P41-RRO0O0785-14

National AIM Project: MENTOR -- MEDICAL EVALUATION OF
THERAPEUTIC ORDERS

Principal Investigators: Stuart Speedie, Ph.D. (SPEEDIE@SUMEX-AIM)
School of Pharmacy
University of Maryland
20 N. Pine Street
Baltimore, Maryland 21201
(301) 528-7650

Terrence F. Blaschke, M.D. (BLASCHKE@SUMEX-AIM)
Department of Medicine

Division of Clinical Pharmacology

Stanford University Medical Center

Stanford, California 94305

Contact: either PI

The goal of the MENTOR project is to implement and begin evaluation of a computer-
based methodology for reducing therapeutic misadventures. The project uses an on-line
expert system to continuously monitor the drug therapy of individual patients and
generate specific warnings of potential and/or actual unintended effects of therapy.
The appropriate patient information is automatically acquired through interfaces to a
hospital information system. This data is monitored by a system that is capable of
employing complex chains of reasoning to evaluate therapeutic decisions and arrive at
valid conclusions in the context of all information available on the patient. The results
teached by the system are fed back to the responsible physicians to assist future
decision making.

Specific objectives of this project include:

1. Implement a prototype computer-based expert system to continuously
monitor in-patient drug therapy that uses a modular medical knowledge base
and a separate inference engine to apply the knowledge to specific situations.

2. Select a small number of important and frequently occurring drug therapy
problems that can lead to therapeutic misadventures and construct a
comprehensive knowledge base necessary to detect these situations.

3. Design and begin implementation of an evaluation of the prototype
MENTOR system with respect to its impact on the on the physicians’
therapeutic decision making as well as its effects on the patient in terms of
specific mortality and morbidity measures.

The work in this project builds on the extensive previous work in drug monitoring

done by these investigators in the Division of Clinical Pharmacology at Stanford and
the University of Maryland School of Pharmacy.

E. H. Shortliffe 234
5P41-RRO00785-14 Scientific Subproject Abstracts

National AIM Project: SOLVER -- PROBLEM SOLVING EXPERTISE

Principal Investigators: Paul E. Johnson, Ph.D.
School of Management and
Center for Research in Human Learning
205 Elliott Hall
University of Minnesota
Minneapolis, Minnesota 55455
(612) 376-2530 (PJOHNSON@SUMEX-AIM)

James R. Slagle, Ph.D.

Department of Computer Science

136 Lind Hall

University of Minnesota

Minneapolis, Minnesota 55455

(612) 373-0132 (SLAGLE@SUMEX-AIM)

William B. Thompson, Ph.D.

Department of Computer Science

136 Lind Hall

University of Minnesota

Minneapolis, Minnesota 55455

(612) 373-0132 (THOMPSON@SUMEX-AIM)

The Minnesota SOLVER project focuses upon the development of strategies for
discovering and representing the knowledge and skill of expert problem solvers.
Although in the last fifteen years considerable progress has been made in synthesizing
the expertise required for solving complex problems, most expert systems embody only a
limited amount of expertise. What is still lacking is a theoretical framework capable of
reducing dependence upon the expert's intuition or on the near exhaustive testing of
possible organizations. Our methodology consists of: (1) extensive use of verbal
thinking aloud protocols as a source of information from which to make inferences
about underlying knowledge structures and processes; (2) development of computer
models as a means of testing the adequacy of inferences derived from protocol studies;
(3) testing and refinement of the cognitive models based upon the study of human and
model performance in experimental settings. Currently, we are investigating problem-
solving expertise in domains of medicine, computer hardware diagnosis, offline quality
control, financial auditing, management, and law.

SOFTWARE AVAILABLE ON SUMEX

A redesigned version of the Diagnoser simulation model, named Gaien, has been
implemented on SUMEX. Galen is an expert system which uses recognition-based
reasoning in pediatric cardiology.

REFERENCES

1. Johnson, P.E., Moen, J.B., and Thompson, W.B.: Garden Path Errors in
Medical Diagnosis. -In Bolc, L. and Coombs, MJ. (Eds.), COMPUTER
EXPERT SYSTEMS, Springer-Verlag (in press).

2. Johnson, P.E., Johnson, M.G., and Little, R.K.: Expertise in trial advocacy:

Some considerations for inquiry into its nature and development, Campbell
Law Review, (in press).

235 E. H. Shortliffe
Scientific Subproject Abstracts $P41-RRO00785-14

3. Johnson, P.E., The Expert Mind: A New Challenge for the Information
scientist, in BEYOND PRODUCTIVITY: INFORMATION SYSTEM
DEVELOPMENT FOR ORGANIZATIONAL EFFECTIVENESS, Th. M. A.
Bemelmans (editor), Elsevier Science Publishers B.V. (North-Holland), 1984.

4. Johnson, P.E.: What kind of expert should a system be? Journal of Medicine
and Philosophy, 8:77-97, 1983.

5. Johnson, P.E., Duran, A., Hassebrock, F., Moller, J., Prietula, M., Feltovich,
P. and Swanson, D.: Expertise and error in diagnostic reasoning. Cognitive
Science 5:235-283, 1981.

6. Simpson, D.E., Rich, E., Dalgaard, K., Gjerdingen, K., Crowson, T., O’Brien,
D., Johnson, P.E.: The diagnostic process in primary care: A comparison of

general internists and family physicians. SOCIAL SCIENCE AND
MEDICINE (in press).

7. Spackman, K.A. and Connelly, D.P.: Knowledge-based systems in laboratory
medicine and pathology: A review and survey of the field. Archives of
Laboratory Medicine and Pathology 111:116-119, 1987.

8. Thompson, W.B., Johnson, P.E. and Moen, J.B.: Recognition-based diagnostic
reasoning. Proc. Eighth IJCAI, Karlsruhe, West Germany, August, 1983.

E. H. Shortliffe 236
5P41-RR00785-14 Scientific Subproject Abstracts

National AIM Project: ATTENDING Project:
A Critiquing Approach to
Expert Computer Advice

Principal Investigator: Perry L. Miller, M.D., Ph.D.
Department of Anesthesiology
Yale University School of Medicine
New Haven, CT 06510
(203) 785-2802

Our project is exploring the "critiquing" approach to bringing computer-based advice to
the practicing physician.

Critiquing is a different approach to the design of artificial intelligence based expert
systems. Most medical expert systems attempt to simulate a physician's decision-making
process. As a result, they have the clinical effect of trying to tell a physician what to
do: how to practice medicine. In contrast, a critiquing system first asks the physician
how he contemplates approaching his patient's care, and then critiques that plan. In the
critique, the system discusses any risks or benefits of the proposed approach, and of any
other approaches which might be preferred. It is anticipated that the critiquing
approach may be particularly well suited for domains, like medicine, where decisions
involve a great deal of subjective judgment.

To date, several prototype critiquing systems have been developed in different medical
domains:

1. ATTENDING, the first system to implement the critiquing approach,
critiques anesthetic management.

2. HT-ATTENDING critiques the pharmacologic management of essential
hypertension.

3. VO-ATTENDING critiques aspects of ventilator management.

4. PHEO-ATTENDING critiques the laboratory and radiologic workup of a
patient for a suspected pheichromocytoma.

5. In addition, a domain-independent system, ESSENTIAL-ATTENDING, has
been developed to facilitate the implementation of critiquing systems in
other domains.

PUBLICATIONS

1. Miller, P.L.: Expert Critiquing Systems: Practice-Based Medical Consultation
by Computer. New York: Springer-Verlag, 1986.

2. Miller, P.L. (Ed.): Selected Topics in Medical Artificial Intelligence. New
York: Springer-Verlag {in press).

3. Miller, P.L., Shaw, C., Rose, J.R., Swett, H.A.: Critiquing the process of
radiologic differential diagnosis. Computer Methods and Programs in
Biomedicine 22:12-25, 1986.

4. Miller, PL: The evaluation of artificial intelligence systems in medicine.
Computer Methods and Programs in Biomedicine 22:5-11, 1986.

237 E. H. Shortliffe
Scientific Subproject Abstracts 5P41-RR00785-14

5. Rennels, G.D., Shortliffe, E.H., Miller; P.L.: Choice of explanation in
medical management: A multi-attribute model of artificial intelligence
approaches. Medical Decision Making 7:22-31, 1987.

6. Mars, N.J.1., Miller, P.L.: Knowledge acquisition and verification tools for
medical expert systems. Medical Decision Making 7:6-11, 1987.

7. Miller, P.L., Blumenfrucht, S.J., Rose, J.R., Rothschild, M., Swett, H.A.,
Weltin, G., Mars, NJ.1: HYDRA: A knowledge acquisition tool for expert
systems which critique medical workup. Medical Decision Making 7:12-21,
1987.

8. Swett, H.A., Miller, PL: ICON: A computer-based approach to differential
diagnosis in radiology. Radiology (in press).

9. Rennels, G.D., Shortliffe, E.H. Stockdale, F.E., Miller, P.L.: A
computational model of reasoning from the clinical literature. Computer
Methods and Programs in Biomedicine (in press).

10. Rennels, G.D., Shortliffe, E.H., Stockdale, F.E., Miller, PL: A structured
representation of the clinical literature and its use in a medical management
advice system. Bulletin du Cancer (in press).

11. Miller, P.L.: Exploring the critiquing approach: Sophisticated practice-based
feedback by computer. Proceedings of the Fifth World Conference on
Medical Informatics: MEDINFO-86, Washington, D.C., October 1986, pp 2-6.

12. Mars, N.J.1., Miller, P.L.: Tools for knowledge acquisition and verification
in medicine. Proceedings of the Tenth Symposium on Computer
Applications in Medical Care, Washington, D.C., October 1986, pp. 36-42.

13. Miller, P.L., Blumenfrucht, 5.J., Rose, J.R., Rothschild, M., Weltin, G., Swett,
H.A., Mars, N.J.I.: Expert system knowledge acquisition for domains of
medical workup: An augmented transition network model. Proceedings of
the Tenth Symposium on Computer Applications in Medical Care,
Washington, D.C., October 1986, pp. 30-35.

14. Rennels, G.D., Shortliffe, E.H., Stockdale, F.E., Miller, P.L.: Reasoning from
the clinical literature: The Roundsman system. Proceedings of the Fifth
World Conference on Medical Informatics: MEDINFO-86, Washington, D.C.,
October 1986, pp. 771-775.

15. Rennels, G.D., Shortliffe, E.H., Stockdale, F.E., Miller, P.L.: Updating an
expert knowledge base as medical knowledge evolves: Examples from
oncology management. Proceedings of the American Association of Medical
Systems and Informatics Congress 87, San Francisco, May 1987, pp. 228-231.

16. Fisher, P.R., Miller, P.L., Swett, H.A.: A script-based representation of
medical knowledge involving multiple perspectives. Proceedings of the
American Association of Medical Systems and Informatics Congress-87, San
Francisco, May 1987, pp. 233-237.

17. Miller, P.L.: Expert consultation systems in medicine: A complex and

fascinating domain. Proceedings of the Annual Meeting of the IEEE
(Electro~87), New York, April 1987, pp. 1/2:1-4 (invited paper).

E. H. Shortliffe 238
5P41-RRO0785-14 Scientific Subproject Abstracts

Stanford Project: REFEREE Project

Principal Investigators: Bruce G. Buchanan, Principal Investigator
Computer Science Department
Stanford University
Stanford, California 94305

Byron W. Brown, Co-Principal Investigator
Department of Medicine

Stanford University Medical Center
Stanford, California 94305

Daniel E. Feldman, Associate Investigator
Department of Medicine

Stanford University Medical Center
Stanford, California 94305

The goals of this project are related both to medical science and Artificial Intelligence:
(a) use AI methods to allow the informed but non-expert reader of the medical
literature to evaluate a randomized clinical trial, and (b) use the interpretation of the
medical literature as a test problem for studies of knowledge acquisition and fusion of
information from disparate sources. REFEREE and REVIEWER, a planned extension,
will be used to evaluate the medical literature of clinical trials to determine the quality
of a clinical trial, make judgements on the efficacy of the treatment proposed, and
synthesize rules of clinical practice. The research is an initial step toward a more
general goal - building computer systems to help the clinician and medical scientist
read the medical literature more critically and more rapidly.

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E. H. Shortliffe
Scientific Subproject Abstracts 5P41-RRO0785-14

National AIM Project: Computer-Aided Diagnosis of

Lymph Node Pathology (PATHFINDER)
Principal Investigator: Bharat Nathwani, M.D.

Department of Pathology

HMR 204

2025 Zonal Avenue

University of Southern California

School of Medicine

Los Angeles, California 90033

(213) 226-7064 (NATHWANI@SUMEX-AIM)

Lawrence M. Fagan, M.D., Ph.D.

Medical Computer Science Group
Department of Medicine

Medical School Office Building

Stanford, California 94305

(415) 723-6979 (FAGAN@SUMEX-AIM)

The PATHFINDER Project is centered on the construction of an expert system for
assisting pathologists with the diagnosis of tissue pathology. PATHFINDER research is
focused on the domain of lymph node pathology. The project is based at the
University of Southern California in collaboration with the Stanford University Medical
Computer Science Group. Ongoing AIM research has been addressing fundamental
problems of knowledge representation, reasoning strategies, user modeling, explanation,
and user acceptance. A pragmatic goal of the project is to provide a valuable diagnostic
and educational tool for pathologists with different levels of training and experience by
integrating diverse knowledge about lymph node pathology. It is hoped that
PATHFINDER basic research on representation and inference in combination with the
pragmatic goals of constructing a clinically-relevant diagnostic aid will lead to useful
advances in medical computing.

A pilot version of the program provides diagnostic advice on eighty common benign
and malignant diseases of the lymph nodes based on 150 histologic features. Our
research plans are to develop a full-scale version of the computer program by
substantially increasing the quantity and quality of knowledge and to develop techniques
for knowledge representation and manipulation appropriate to this application area. The
design of the program has been strongly influenced by the INTERNIST/CADUCEUS
program developed on the SUMEX resource.

SOFTWARE AVAILABLE ON SUMEX
PATHFINDER-- A version of the PATHFINDER program is available for

experimentation on the DEC 2060 computer. This version is a pilot
version of the program, and therefore has not been completely tested.

E. H. Shortliffe 240
5P41-RRO0785-14 Scientific Subproject Abstracts.

AIM Pilot Project: RXDX Project

Principal Investigators:
Robert Lindsay, Ph.D. (313) 764-4227
Michael Feinberg, M.D., Ph.D. (215) 842-4208
University of Michigan
Ann Arbor, Michigan

We are developing a prototype expert system that could act as a consultant in the
diagnosis and management of depression. Health professionals will interact with the
program as they might with a human consultant, describing the patient, receiving advice,
and asking the consultant about the rationale for each recommendation. The program
uses a knowledge base constructed by encoding the clinical expertise of a skilled
psychiatrist in a set of rules and other knowledge structures. It will use this knowledge
base to decide on the most likely diagnosis (endogenous or nonendogenous depression),
assess the need for hospitalization, and recommend specific somatic treatments when
this is indicated (e.g., tricyclic antidepressants). The treatment recommendation will
take into account the patient's diagnosis, age, concurrent illnesses, and concurrent
treatments (drug interactions).

The potential. benefits to psychiatry include: making relatively skilled psychiatric
consultation widely available in underserved areas, including some public mental health
facilities where patients are seen by non-psychiatrists and have relatively little direct
patient-physician contact; providing mnon-psychiatrically trained physicians with
additional information about psychiatric diagnosis and treatment; avoiding errors of
oversight caused by inaccessible patient data; and increased productivity in patient care.
Like any good consultant, the program will be able to teach the interested user, and can
function as a teaching tool independent of direct clinical application.

PUBLICATIONS

1. Feinberg, M.and Lindsay, R.K.: Expert systems in Psychiatry and
Psychopharmacology. Psychopharmacol. Bull., 22, 1986, 311-316.

2. Lindsay, R. K. Expert Systems in Psychiatric Diagnosis: Rule-Based Systems.
Presented at MediInfo86, Washington, D.C.

3. Feinberg, M. What Psychiatrists Can't Do. Presented at Medinfo86,
Washington, D. C.

241 E. H. Shortliffe
Scientific Subproject Abstracts 5P41-RRO00785-14

National AIM Project: DECISION SUPPORT FOR
TIME-VARYING CLINICAL PROBLEMS

Principal Investigator: Lawrence Widman, M.D., Ph.D.
Division of Cardiology
Case Western Reserve University
2065 Adelbert Road
Cleveland, OH 44106
(216) 844-3153

Time-varying systems, which include many areas of medicine, science, economics, and
business, can be described mathematically by differential equations. They are distinct
from the pattern-matching and logic-based domains dealt with so successfully by
existing expert system methods, because they can include feedback relationships. It is
generally felt that they are best approached by enhancement of existing methods for
deep model-based reasoning.

The goal of this project is to develop AI methods for capturing and using knowledge
about time-varying systems. The strategy is to address general problems in model-based
knowledge representation and reasoning. The intermediate objective is to develop
methods which are powerful enough to work in selected realistic situations yet are
general enough to be transportable to other, unrelated knowledge domains.

The tactical approach is to work on well-defined yet complex and interesting problems
in the medical domain. We have, therefore, selected the human cardiovascular system
as our prototype of a time-varying system, and are developing methods for representing
and reasoning about its mechanical and electrical activities in the normal and diseased
states.

REFERENCES

1. Widman, L.E. Reasoning about Diagnosis and Treatment in a Causal
Medical Model using Semi-Quantitative Simulation and Inference.
Workshop on Artificial Intelligence in Medicine, National Conference on
Artificial Intelligence, AAAI-87, Seattle.

2. Widman, L.E., Lee, Y.-B., and Y.-H. Pao. Diagnosis of Causal Models by
Semi-Quantitative Reasoning (submitted to SCAMC 1987).

3. Widman, L.E., Lee, Y.-B. and Y.-H. Pao. Diagnosis of Causal Medical
Models by Semi-Quantitative Reasoning. In: Miller, P.L. (ed.) Topics in
Medical Artificial Intelligence, Springer-Verlag (in preparation).

4. Lee, Y.-B. and L.E. Widman. Reasoning about Diagnosis and Treatment in
a Causal Time-varying Domain using Semi-Quantitative Simulation and
Inference. Workshop on Artificial Intelligence and Simulation, National
Conference on Artificial Intelligence, AAAI-86, Philadelphia.

5. Widman, L.E. Representation Method for Dynamic Causal Knowledge Using

Semi-Quantitative Simulation. Fifth World Conference on Medical
Informatics. 1986: 180-184.

E. H. Shortliffe 242
5P41-RRO00785-14 Scientific Subproject Abstracts

National AIM Project: KNOWLEDGE ENGINEERING FOR
RADIATION THERAPY

Principal Investigator: Ira J. Kalet, Ph.D.
School of Medicine
University of Washington at Seattle
Seattle, Washington 98195
(206) 548-4107

We are developing an expert system for planning of radiation therapy for head and
neck cancers. The project will ultimately combine knowledge-based planning with
numerical simulation of the radiation treatments. The numerical simulation is needed
in order to determine if the proposed treatment will conform to the goals of the plan
(required tumor dose, limiting dose to critical organs).. The space of possible radiation
treatments is numerically very large, making traditional search techniques impractical.
Yet, with modern radiation therapy equipment, the design of treatment plans might be
significantly aided by automatically generating plans that meet the treatment constraints.
The project will result in systematization of knowledge about radiation treatment design,
and will also provide an example of how to represent and solve design problems with a
knowledge based system.

This project has some relevance to computer science as well, in that our approach, if
successful, may contribute to a better understanding of design problem solving with
knowledge-based systems.

REFERENCES

I. Kalet and W. Paluszynski: A Production Expert System for Radiation Therapy
Planning. Proceedings of the AAMSI Congress 1985, May 20-22, 1985, San Francisco,
California. Edited by Allan H. Levy and Ben T. Williams. American Association for
Medical Systems and Informatics, Washington, D.C., 1985.

W. Paluszynski and I. Kalet: Radiation Therapy Planning: A Design Oriented Expert
System. WESTEX-87 (Western Conference on Expert Systems), Anaheim, California,
June 2-4, 1987.

I. Kalet and J. Jacky: Knowledge-based Computer Simulation for Radiation Therapy

Planning. Proceedings of the Ninth International Conference on the use of Computers
in Radiotherapy, Scheveningen, the Netherlands, June 1987. North Holland, 1987.

243 E. H. Shortliffe
Scientific Subproject Abstracts 5P41-RRO0785-14

AIM Pilot Project: COMPUTER-BASED EXERCISES IN
PATHOPHYSIOLOGIC DIAGNOSIS
Principal Investigator: J. Robert Beck, M.D.
MIS Butler I

2 Maynard St.
Dartmouth College
School of Medicine
Hanover, NH 03755
(603) 646-7171

Research in artificial intelligence at Dartmouth Medical School focuses on three main
areas: 1) knowledge-based systems applied to laboratory medicine and pathology, 2)
knowledge acquisition using machine learning techniques, and 3) computer-based
instruction using artificial intelligence techniques to critique students’ workup plans.
These projects have in common the fundamental research questions of how knowledge
should be represented and used in a classification approach to problem-solving related
to the use of laboratory data.

An interdisciplinary team of computer scientists, physicians, and educators is working
on the Computer-based Exercises project. A prototype system is nearing completion,
with formative evaluation scheduled for Fall, 1987.

REFERENCES

Beck, J.R., Prietula, M.J., Russo, E.A.: A role for intelligent systems in teaching medical
pathophysiology. In: Salamon, R., Blum, B., Jorgenson, M. (eds). Proc. Fifth Conf.
Med. Inform. (MEDINFO '86), Elsevier-North Holland, Amsterdam, 1986, 936-938.

Beck, J.R.: Artificial intelligence: A topic for Medical Decision Making? (edit.) Med.
Decis. Making 1987; 7:4.

E. H. Shortliffe 244
5P41-RRO00785-14 References

References

1. Barr, Avron, Paul R. Cohen and Edward A. Feigenbaum. The Handbook of
Artificial Intelligence, Volumes I, II, and III, William Kaufmann, Inc., Los Altos, CA,
1981] and 1982.

2. Congressional Research Service, Library of Congress. The Impact of Information
Technology on Science. Science Policy Study Background Report Number 5, Task Force
on Science Policy, Committee on Science and Technology, U.S. House of
Representatives, September, 1986.

3. Coulter, C. L. "Research Instrument Sharing.” Science 201, 4354 (1978).

4. Harmon, Paul and King, David. Expert Systems - Artificial Intelligence in
Business. John Wiley & Sons, Inc., New York, NY, 1985.

5. Hayes-Roth, F., Waterman, D. A. & Lenat, D. B. (Eds.). Building Expert Systems.
Addison-Wesley, Reading, MA, 1983.

6. Horvitz, E.J., Heckerman, D.E., and Langlotz, C.P. A Framework for Comparing
Alternative Formalisms for Plausible Reasoning. Proceedings of AAAI-86, Fifth
National Conference on Artificial Intelligence, American Association for Artificial
Intelligence, Menlo Park, CA, August, 1986, pp. 210-214.

7. Information Technology R&D Project Staff. Information Technology R&D: Critical
Trends and Issues (Summary). Report OTA-CIT-269, Congressional Office of
Technology Assessment, October, 1985.

8. Keith A. Lantz et. al. V - System 4.1 Reference Manual. December 1, 1983.
Computer Systems Laboratory, Stanford University.

9. Keith A. Lantz, David R. Cheriton, and William I. Nowicki. Third Generation
Graphics for Distributed Systems. Computer Systems Laboratory. Stanford University,
December 21, 1982

10. Lederberg, J. “Digital Communications and the Conduct of Science: The New
Literacy.” Proceedings of the [EEE 66, 11 (1978).

11. Nilsson, N.. Principles of Artificial Intelligence. Tioga, Palo Alto, CA, 1980.

12. NLM Planning Panel 4. NLM Long Range Plan: Medical Informatics. Shortliffe,
E.H., Panel 4 Chairman , National Library of Medicine, January, 1987.

13. Rich, E.. Artificial Intelligence. McGraw-Hill, New York, 1983.

14. Paul S. Rosenbloom, John E. Laird. Mapping Explanation-Based Generalization
onto SOAR. Proceedings of the Fifth National Conference on Artificial Intelligence,
American Association for Artificial Intelligence, Philadelphia, Penn., August

11-15, 1986, pp. 561-567. Also KSL-86-46 and STAN-CS-86-1111

15. Scheifler, R.W. and Gettys, J. The X Window System. Draft October 1986. To
appear in a special issue of the ACM Transactions on Graphics -- user interface
software

245 E. H. Shortliffe
5P41-RRO0785-14

16. Steele, Guy L., Jr... Common Lisp - The Language. Digital Press, Burlington, MA,
1984.

17. NeWS Preliminary Technical Overview. Sun Microsystems, Inc., 1986.

18. Waterman, Donald A.. A Guide to Expert Systems. Addison-Wesley Publishing
Company, Menlo Park, CA, 1986.

19. David C. Wilkins and Bruce G. Buchanan. On Debugging Rule Sets When
Reasoning Under Uncertainty. Tech. Rept. KSL 86-30, Stanford University, Knowledge
Systems Laboratory, July, 1986. Earlier version appeared in Proceedings of the Fifth
National Conference on Artificial Intelligence, August 1986

20. David C. Wilkins, Willian J. Clancey and Bruce G. Buchanan. Using and
Evaluating Differential Modeling in Intelligent Tutoring and Apprentice Learning
Systems. Tech. Rept. KSL 86-62, Stanford University, Knowledge Systems Laboratory,
October, 1986.

21. David C. Wilkins. Knowledge Base Debugging Using Apprenticeship Learning
Techniques. Tech. Rept. KSL 86-63, Stanford University, Knowledge Systems
Laboratory, December, 1986.

22. David C. Wilkins, William J. Clancey and Bruce G. Buchanan. Knowledge Base
Refinement Using Abstract Control Knowledge. Tech. Rept. KSL 87-01, Stanford
University, Knowledge Systems Laboratory, January, 1987.

23. Winston, P.. Artificial Intelligence, 2nd ed. Addison-Wesley, Reading, MA, 1984.

E. H. Shortliffe 246