Scientific Programme
[Tutorial Programme] [Invited Speakers]
[Accepted Papers] [Accepted Abstracts]
[Accepted Posters]
Conference Schedule
For further information regarding the Conference Schedule, please follow this link: http://www.ai.univie.ac.at/aimdm99/schedule.html
Tutorial Programme
Half day tutorials will be organized on Sunday 20th June in parallel sessions. Morning tutorials will take place from 9 am to 12:30 pm and afternoon tutorials from 1:30 pm to 5 pm. Prices will be 400 DKK for one tutorial and 800 DKK for two. For registration, please follow this link. Proposals for tutorials are invited. Send proposals to the tutorials chair, Jeremy Wyatt (see Conference addresses) no later than December 15th 1998.
Please notice following: Tutorials 1, 4 and 5 runs in the morning and tutorials 2, 3 and 6 in the afternoon.
1) NATURAL LANGUAGE GENERATION
Audience: Researchers and software developers who are interested in improving the quality of documents and other written materials produced by software systems.
Description: Many medical IT systems need to produce documents or other types of written texts, such as discharge reports, letters to patients, and explanations of expert-system reasoning. The quality and readability of such texts is not always as high as it could be, unfortunately. This tutorial will discuss some of the linguistic problems that computer-generated texts can suffer from, such as poor rhetorical structure, inappropriate anaphors, false implicatures, and grammatical mistakes. Natural-language generation (NLG) technology is introduced, which can be used to automatically produce texts which satisfy linguistic constraints and hence do not suffer from these problems. The tutorial will be illustrated with examples from NLG systems developed at Aberdeen and elsewhere. Attendees do not need any background in linguistics or natural-language processing, but they should be familiar with basic AI concepts. Even people who do not intend to use NLG technology will still benefit from the tutorial, by becoming more aware of potential linguistic problems in computer-generated texts and how they can be resolved.
Tutorial Presenter:
Ehud Reiter
Dept of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK.
Phone +44-1224-273443, Fax +44-1224-273422,
email: ereiter@csd.abdn.ac.uk
2) THE PSYCHOLOGY OF MEDICAL DECISION MAKING
This course is intended for clinicians, and others who wish to gain insight into the psychological factors influencing their decision making under uncertainty. The aim of this tutorial is to increase understanding of the psychological processes involved in medical decision making. This knowledge is useful in trying to improve decision making. Additionally it is a fruitful area for research. The course assumes the attendee has only a basic knowledge of the subject matter.
The clinician's reasoning is a partial cause of non-optimal medical decisions. The cognitive psychology of judgment and decision making offers explanations of how some of these reasoning errors are made. The course will review the basic nature of expert medical reasoning, to discover possibilities for capitalizing on its strengths and supporting its weaknesses. The participant will learn why the human cognitive system, with its large memory, limited attention span, and powerful pattern recognition ability, seems destined to operate by automatic "scripted" response rather than thoughtful deliberation.
We will demonstrate the implications of clinicians' cognitive processes for two basic activities of rational decision making:
diagnosis and choosing a course of action. Clinicians' reasoning strategies, motivations, habits, and cognitive limitations can lead them to make errors of diagnosis, and define the methods they can use to seek and use information more rationally. Clinicians' strategies for predicting what will happen can lead to misjudgments of probability, and their methods of evaluating things can lead to misjudgments of treatment consequences. Understanding the psychological processes involved will suggest methods for helping clinicians reason better about the probabilities of outcomes and about their own or their patients' preferences.
Tutorial presenters:
Robert M. Hamm
Clinical Decision Making Program, Department of Family and Preventive Medicine,
University of Oklahoma Health Sciences Center,
900 NE 10th St, Oklahoma City, OK 73104, U.S.A.
Telephone: 405/271-8000 ext3-2302, Fax: 405/271-2784,
email: robert-hamm@ouhsc.edu
Clare Harries
Department of Psychology, University College London,
Gower Street,London, WC1E 6BT, U.K.
Telephone: +44 171 504 5389, Fax: +44 171 436 4276,
email: clare.harries@ucl.ac.uk
Jack Dowie, PhD
Department of Applied Social Sciences, The Open University,
Milton Keynes, MK7 6AA, U.K.
Telephone: +44 171 254 7576, Fax: +44 171 254 7576,
email: j.a.dowie@open.ac.uk
3) DATA MINING TECHNIQUES AND APPLICATIONS IN MEDICINE
With the widespread use of medical information systems that include databases which have recently featured explosive growth in their sizes, physicians and medical researchers are faced with a problem of making use of the stored data. The traditional manual data analysis has become insufficient, and methods for efficient computer-assisted analysis indispensable, in particular those of data mining and other related techniques of knowledge discovery in databases and intelligent data analysis.
This tutorial will address current techniques and applications of data mining in medicine. We will provide an overview of data mining methods, including symbolic data mining (mining of decision rules, association rules, decision trees, inductive logic programming, hierarchical concept discovery, etc.) and subsymbolic data mining (instance based learning, neural nets, Naive Bayesian classifier, etc). Specific evaluation techniques and statistical criteria suited for medical applications will be discussed. Selected data preprocessing and data visualization methods will also be presented.
The participants of tutorial will get familiar with:
- fundamental concepts data mining and knowledge discovery in data bases
- an overview of data mining methods,
- specific data mining methods, including decision trees and rules, association rules, and
naive Bayesian classifier
- metrics that can be used to assess the quality and interestingness of discovered relationships
- how intelligent data analysis is different from common statistical approaches and how it can complement it
- what features should be supported by a particular data mining tool to be useful for medical data analysis
- how to successfully integrate data mining techniques within existing medical information system
Intended audience:
This tutorial will be of interest to clinicians, medical researchers, information technology professionals, information systems developers and managers, data analysts and institutional decision makers, and anyone else interested in applying modern data analysis methods to extract useful knowledge from medical data bases.
Tutorial presenters:
Blaz Zupan (1,2) and Nada Lavrac (2)
(1) University of Ljubljana,
Faculty of Computer and Information Sciences Trzaska 25,
SI-1000 Ljubljana, Slovenia.
Telephone: +386 61 177 3380, fax: +386 61 125 1038
email: blaz.zupan@fri.uni-lj.si
(2) J.Stefan Institute, Department of Intelligent Systems Jamova 39,
SI-1000 Ljubljana, Slovenia.
Telephone: +386 61 177 3272, fax: +386 61 125 1038.
email: nada.lavrac@ijs.si
4) HOW TO BUILD A CAUSAL PROBABILISTIC NETWORK
A Causal Probabilistic Network, also called Bayesian network is a flexible and efficient framework for reasoning under uncertainty, and it has established itself as a practical method for knowledge representation and inference in a number of medical areas. The framework consists of a structural part, where the domain in question is modelled through a directed acyclic graph, and a quantitative part, where the impact between nodes in the graph are represented as conditional probabilities. This tutorial will through examples give an informal introduction to theory and use of CPNs in connection with decision theory. The participants will obtain hands-on experience with the construction of a small CPN, including the acquisition of structure and conditional probabilities.
Tutorial presenters:
Finn V. Jensen
Dept. of Computer Science, Aalborg University,
Fredrik Bajers Vej 7,DK-9000 Aalborg Øst, Denmark.
Telephone: +4596358903,
email: fvj@cs.auc.dk
Steen Andreassen
Dept. of Medical Informatics and Image Analysis, Aalborg University,
Fredrik Bajers Vej 7D, DK-9000 Aalborg Øst, Denmark.
Telephone: +4596358812, Fax: +4598154008,
email: sa@miba.auc.dk
5) FOUNDATIONS OF PREFERENCE THEORY AND QUALITY OF LIFE ADJUSTMENT.
The methods of preference assessment and quality of life adjustment are widely applied in the medical decision making and
cost-effectiveness literature. Yet, the theory and assumptions that underlie the use of these methods are poorly understood. The objectives of this short course are to provide experienced practioners with a quick and accessible introduction to the underpinnings of utility theory, with an emphasis on the relevance, power, and limitations of these assumptions in health and medical contexts. Topics to be covered will include: the theory of choice and preference; traditional models of individual decision making under uncertainty, including the von-Neumann - Morgenstern expected utility framework; the additional assumptions that support the use of multi-attribute utility functions and quality-adjusted life-years; and the difficulties encountered when the theory is extended beyond the individual to represent choice at the societal level.
Tutorial presenter:
Jospeh S. Pliskin, Ph.D.
Department of Industrial Engineering and Management and Department of Health Policy and Management,
Ben-Gurion University of the Negev,Beer-Sheva, Israel.
P.O. Box 653, Beer-Sheva 84105, Israel.
Telephone: 972-7-6472219,Fax: 972-7-6472958
email: jpliskin@bgumail.bgu.ac.il
6) HOW TO READ (AND MAYBE PERFORM) A SYSTEMATIC REVIEW (METAANALYSIS)
Physicians are committed to manage their patients according to the best available evidence. Systematic reviews are about asking the relevant questions; obtaining the published material (all of it); and extracting the evidence. In the tutorial we will address the following questions:
- Why do we need systematic reviews?
- How to put the questions?
- How to formulate a relevant protocol?
- How to collect the pertinent studies?
- How to evaluate the methodological soundness of the studies? Does
it matter?
- How to obtain data from the studies and how to combine it?
- How to explore heterogeneity and why is it so important?
- How to check for biases?
- How to present results?
- Does metaanalysis work?
Tutorial presenters:
Karla Soares Weiser, Leonard Leibovici
Department of Medicine E, Beilinson Hospital,
Petah-tiqva 49100, Israel.
Telephone 972 3 9376501; fax 972 3 9376505;
email: leibovic@post.tau.ac.il
Invited Speakers
In addition to the above activities AIMDM´99 includes
lectures from eminent speakers in the fields of Artificial
Intelligence and decision making in medicine. Preliminary titles
for these lectures are:
- Artificial Intelligence for Learning Health Care
Organizations.
Prof. Mario Stefanelli
Laboratory of Medical Informatics, Department of
Computers and System Science.
University of Pavia, Italy.
- From Clinical Guideliness to Decision Support.
Prof. Gianpaolo Molino
Department of Internal Medicine.
Azienda Ospedaliera San Giovanni Battista di Torino,
Italy.
- Machine Learning for Data Mining in Medicine.
Prof. Nada Lavrac
Department of Intelligent Systems.
J. Stefan Institute, Lubljana, Slovenia.
- Timing is Everything: Temporal Reasoning and Temporal
Data Maintenance in Medicine.
Prof. Yuval Shahar
Medical Informatics.
Stanford University School of Medicine, California, USA.
Accepted Papers (25 mins oral presentation)
- A Decision Theoretic Approach to Empirical Treatment of Bacteraemia Originating from the Urinary Tract - Andreassen,S., Leibovici,L., Schonheyder,H.C., Kristensen,B.,Riekehr,C., Kjaer,A.G., Olesen,K.G.
- Intelligent Analysis of Clinical Time Series by Combining Structural Filtering and Temporal Abstractions - Bellazzi,R., Larizza,C., Magni,P., Montani,S., Nicolao,G.De
- From Description to Decision: Towards a Decision Support System for MR Radiology of the Brain - Boulay,B.du, Teather,B., Boulay,G.du, Jeffrey,N., Teather,D.,Sharples,M., Cuthbert,L.
- A Model-Based Approach for Learning to Identify Cardiac Arrhythmias - Carrault,G., Cordier,M.-O., Quiniou,R., Garreau,M., Bellanger,J.J.,Bardou,A.
- A Conversational Model for Health Promotion on the World Wide Web - Cawsey,A., Grasso,F., Jones,R.
- Visualizing Temporal Clinical Data on the WWW - Combi,C., Portoni,L., Pinciroli,F.
- Integrating Deep Biomedical Models into Medical Decision Support Systems: An Interval Constraint Approach - Cruz,J., Barahona,P., Benhamou,F.
- An ECG Ischaemic Detection System Based on Self-Organizing Maps and a Sigmoid Function Pre-Processing Stage - Fernandez,E.A., Presedo,J.
- A Multi-Agent System for MRI Brain Segmentation - Germond,L., Dojat,M., Taylor,C., Garbay,C.
- Knowledge-Based Event Detection in Complex Time Series Data - Hunter,J., McIntosh,N.
- Neural Network Recognition of Otoneurological Vertigo Diseases with Comparison of Some Other Classification Methods - Juhola,M., Laurikkala,J., Viikki,K., Auramo,Y., Kentala,E.,Pyykkoe,I.
- Modelling Blood Vessels of the Eye with Parametric L-Systems Using Evolutionary Algorithms - Kokai,G., Toth,Z., Vanyi,R.
- Visualization Techniques for Time-Oriented, Skeletal Plans in Medical Therapy Planning - Kosara,R., Miksch,S.
- Machine Learning in Stepwise Diagnostic Process - Kukar,M., Groselj,C.
- An Intelligent System for Pacemaker Reprogramming - Lucas,P., Tholen,A., Oort,G.van
- Refinement of Neuro-Psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning - Mani,S., Dick,M.B., Pazzani,M.J., Teng,E.L., Kempler,D.,Taussig,I.M.
- The Analysis of Head Inquiry Data Using Decision Tree Techniques - McQuatt,A., Andrews,P.J.D., Sleeman,D.,Corruble,V., Jones,P.A.
- Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data - Miksch,S., Seyfang,A.,Horn,W., Popow,C.
- Multi-Modal Reasoning in Diabetic Patient Management - Montani,S., Bellazzi,R., Portinale,L., Riva,A.,tefanelli,M.
- Internet-Based Decision-Support Server for Acute Abdominal Pain - Ohmann,C., Eich,H.P.
- A Medical Ontology Library that Integrates the UMLS Metathesaurus - Pisanelli,D.M., Gangemi,A., Steve,G.
- Guidelines-Based Workflow Systems - Quaglini,S., Mossa,C., Fassino,C., Stefanelli,M., Cavallini,A.,Micieli,G.
- Types of Knowledge Required to Personalize Smoking Cessation Letters - Reiter,E., Robertson,R., Osman,L.
- Small is Beautiful - Compact Semantics for Medical Language Processing - Romacker,M., Schulz,S., Hahn,U.
- Experiences with Case-Based Reasoning Methods for Medical Knowledge-Based Systems with Special Focus on the Role of Prototypes - Schmidt,R., Pollwein,B., Gierl,L.
- Enhancing Clinical Practice Guidelines Compliance by Involving Physicians in the Decision Process - Seroussi,B., Bouaud,J., Antoine,E.-C.
- Machine Learning for Survival Analysis: A Case Study on Recurrence of Prostate Cancer - Zupan,B., Demsar,J.,Kattan,M., Beck,J.R., Bratko,I.
Accepted Abstracts (15 mins oral presentation)
- Economic Evaluation of the Interest of the Early Screening and Management of the Hypothyroidism: The French Case - Allenet,B., Lenne,X., Laurent,P., Lebrun,T., Wemeau,J.L.
- Intelligent Communication in Medical Care - Birkhoelzer,T., Haft,M., Hofmann,R., Horn,J., Pellegrino,M.,Tresp,V.
- A Web-Based Approach to Collaborative Knowledge Acquisition - Boegl,K., Adlassnig,K.-P.
- Choice of Starting Conditions and Time Frame in Markov Models for Chronic Conditions - Borg,S., Ericsson,K.
- How Useful is the EPR in the Development of Decision-Support Systems? - Bruijn,N.de, Lucas,P., Schurink,K.,Pols,M.
- A Randomized Controlled Trial of Two New Strategies to Improve the Appropriateness and Efficacy of Referral for Lower Third Molar Treatment - Goodey,R.
- Separate Measurement of FN and FP Correlations Between 2 Diagnostic Tests Permits Clinical Probability Revision - Hamm,R.M.
- Models of Medical Decision Making: Regression versus Fast and Frugal Modelling Techniques - Harries,C.,Dhami,M.K.
- Development and Evaluation of a Knowledge-Based System for the Interpretation of Urine Protein Diagnostics - Ivandic,M., Hofmann,W., Guder,W.G.
- What Do Cancer Patients Expect from Radiotherapy? Expectations and Their Relations to Patients' Quality of Life and Physicians' Judgements - Koller,M., Wagner,K., Hoffmann,S., Engenhart-Cabillic,R., Lorenz,W.,Rothmund,M.
- Patients' Perception of the Value of Lower Third Molar Surgery - Liedholm,R., Knutsson,K., Lysell,L., Rohlin,M.,Armstrong,R.,Brickley,M.
- MUST: Multiple Sclerosis Trial Support System - Lottman,P.E.M., Wilt,G.J.Van der, Vries,R.P.F.de, Groenewoud,J.M.M.,
Jongen,P.J.H.
- Screening for Factor V Leiden at the First Episode of Deep Vein Thrombosis: a Cost-Effectiveness Analysis - Marchetti,M., Barosi,G.
- CODA (Consultant Opinion by Decision Analysis): a 'Clinical Guidance Tree' for Oophorectomy Decision Making - Pell,I., Dowie,J.
- A Knowledge-Based Weaning Assistant for Controlling an Artificial Respirator - Semmel-Griebeler,T., Uthmann,T., Weiler,N., Perl,J., Heinrichs,W.
Accepted Abstracts (poster session)
- DEMO: Interactive Multimedia Web-based Physician Education on Hereditary Hemochromatosis - Barash,C.I.
- Triggering 'The Need to Know': Optimizing Genetic Technology in Clinical Practice - Barash,C.I.,Hayflick,S.J.
- Evaluation of Strategies for the Management of Pericoronitis in the United Kingdom - Brickley,M.R.
- Using Sensitivity Analysis for Efficient Quantification of a Belief Network - Coupe,V.M.H., Peek,N., Ottenkamp,J.,Habbema,J.D.F.
- Using Decision Analysis in Extracorporeal Life Support - Gomez,M., Mateos,A., Bielza,C., Rios-Insua,S.
- Mathematical Approach to Measures of Hospital Efficiency - Honeyman,L.
- Experience with a Decision Support Computer Program (DSCP Version 3.0) to Guide Fibrinolyttic Therapy of Suspected Acute Myocardial Infarction (AMI) - Kellett,J.
- A Human-Centered Intelligent Multimedia Multi-Agent Clinical Diagnostic and Treatment Support System - Khosla,R., Phillips,D.
- Upgrading of the Kabisa Didactic Computer Programme for Tropical Medicine: Problems and Solutions - Lagana,S., Bisoffi,Z., Ende,J.Van den
- Using Routine Data to Inform Decision Making in a District General Hospital - O'Shaughnessy,N., Twaddle,N., Honeyman,A.L.
- A Method to Elicit Patient Preferences for Exercise After Cardiac Events - Ruland,C.M., Moore,S.M.
- Selecting Patients for Influenza Vaccination: A Memory-Based Learning Approach - Weeber,M., Vries,C.S.de, Jong - van den Berg,L.T.W.de
Accepted Posters
- The Use of the UMLS Knowledge Sources for the Design of a Domain Specific Ontology: a Practical Experience in Blood Transfusion - Achour,S., Dojat,M., Brethon,J.-M., Blain,G., Lepage,E.
- Achieving Efficient Cooperation in a Hospital Patient Scheduling System - Aknine,S., Aknine,H.
- Speech Driven Natural Language Understanding for Hands-Busy Recording of Clinical Information - Barker,D.J., Lynch,S.C., Simpson,D.S., Corbett,W.A.
- Influence Diagrams for Neonatal Jaundice Management - Bielza,C., Rios-Insua,S., Gomez,M.
- ICU Patient State Characterisation Using Machine Learning in a Time Series Framework - Calvelo,D., Chambrin,M.-C., Pomorski,D., Ravaux,P.
- Electronic Drug Prescribing and Administration - Bedside Medical Decision Making - Clark,I.R., McCauley,B.A., Young,I.M., Nightingale,P.G., Peters,M., Richards,N.T., Adu,D.
- A Comparison of Linear and Non-Linear Classifiers for the Detection of Coronary Artery Disease in Stress-ECG - Dorffner,G., Leitgeb,E., Koller,H.
- Diagnostic Rules of Increased Reliability for Critical Medical Applications - Gamberger,D., Lavrac,N., Groselj,C.
- The Case Based Neural Network Model and Its Use in Medical Expert Systems - Goodridge,W., Peter,H., Abayomi,A.
- Animating Medical and Safety Knowledge - Hammond,P., Wells,P., Modgil,S.
- Neonatal Ventilation Tutor (VIE-NVT), a Teaching Program for the Mechanical Ventilation of Newborn Infants - Horn,W., Popow,C., Stocker,C., Miksch,S.
- Active Shape Models for Customised Prosthesis Design - Hutton,T.J., Hammond,P., Davenport,J.C.
- Representing Knowledge Levels in Clinical Guidelines - Molino,G., Terenyiani,P., Raviola,P., Torchio,M., Bruschi,O.,
Marzuoli,M.
- Application of Therapeutic Protocols: A Tool to Manage Medical Knowledge - Sauvagnac,C., Stines,J., Lesur,A., Falzon,P., Bey,P.
- Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit - Sierra,B., Serrano,N., Larranaga,P., Plasencia,E.J., Inza,I., Jimenez,J.J., Rosa,J.M.Dela, Mora,M.L.
- A Decision-Support System for the Identification, Staging and Functional Evaluation of Liver Diseases (HEPASCORE) - Torchio,M., Battista,S., Bar,F., Pollet,C., Marzuoli,M., Bucchi,M.C., Pagni,R., Molino,G.
- A Screening Technique for Prostate Cancer by Hair Chemical Analysis and Artificial Intelligence - Wu,P., Heng,K.L., Yang,S.W., Chen,Y.F., Mohan,R.S., Lim,P.H.C.
- A Life-Cycle Based Authorisation Expert Database System - Ying-Lie,O.
- Automatic Acquisition of Morphological Knowledge for Medical Language Processing - Zweigenbaum,P., Grabar,N.