Programme Committee ChairSilvia Miksch, Vienna, Austria

Organising Committee ChairJim Hunter, Aberdeen, UK

Artificial Intelligence

inMedicine

Where ...

AIME 05was hosted by the

Department of

Computing Science,

University of Aberdeen

Participants

AustraliaAustralia 22AustriaAustria 99CanadaCanada 55CyprusCyprus 11Czech Rep.Czech Rep. 11DenmarkDenmark 44FranceFrance 19 19GermanyGermany 66GreeceGreece 11

HungaryHungary 11ItalyItaly 15 15IsraelIsrael 11LithuaniaLithuania 22Netherlands 12Netherlands 12NigeriaNigeria 22PolandPoland 11RussiaRussia 11

SloveniaSlovenia 33South KoreaSouth Korea 33SpainSpain 44SwedenSweden 33SwitzerlandSwitzerland 22ThailandThailand 22UKUK 13 13USAUSA 88

42% students42% students

120 registrations for main conference16 for pre-conference events

New Event

Doctoral Consortium Chair: Elpida Keravnou (Nicosia, Cyprus)

8 PhD Students

Participating FacultyAmeen Abu-Hanna (Amsterdam,

Netherlands)Riccardo Bellazzi (Pavia, Italy)Carlo Combi (Verona, Italy)Michel Dojat (Grenoble, France)Peter Lucas (Nijmegen, Netherlands)Silvana Quaglini (Pavia, Italy)Yuval Shahar (Beer Sheva, Israel)

Tutorials

Evolutionary Computation Approaches to Mining Biomedical Data John Holmes (University of Pennsylvania, USA)

Causal Discovery from Biomedical DataSubramani Mani (University of Pittsburg, USA)

Applied Data Mining in Clinical ResearchJohn Holmes (University of Pennsylvania, USA)

Workshops

IDAMAP-2005: Intelligent Data Analysis in Medicine and PharmacologyNiels Peek (University of Amsterdam, Netherlands)John Holmes (University of Pennsylvania, USA)

Biomedical Ontology EngineeringJeremy Rogers, Alan Rector, Robert Stevens (University of Manchester, UK)

90

79

65

148

22

32

45

12.37.2 6.1

9.9

33

0

20

40

60

80

100

120

140

160

1999 2001 2003 2005

AIME years

num

ber

of

subm

issi

ons,

PC

mem

bers

, re

view

/PC m

em

ber

# of submission# of PC members# of reviews/ PC member

Submissions and Reviewing

2005 – Reviewing ProcessPC Members:

+ 13 personsincl. 5 physcians

additional reviewersapprox. 3 reviews / paper

128 % more submissions

Acceptance Rates

2005:35 long 33 short

47.0

39.2

36.9

23.6

33.0

38.0

40.0

22.3

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

1999 2001 2003 2005

year

acce

pta

nce

rat

e

long papers

short papers

AIMDM 99= AIME + ESMDM

Accepted Papers by Country

2

3

5

5

9

1

1

1

2

15

10

3

1

1

6

14

1

1

1

4

1

4

4

4

2

2

1

2

10

4

9

19

0

1

3

4

0

0

1

0

0

8

4

1

1

1

3

11

1

1

0

1

1

3

0

1

1

2

0

2

9

0

45

0 2 4 6 8 10 12 14 16 18 20

Algeria

Australia

Austria

Canada

China

Croatia

Czech Republic

Denmark

Finland

France

Germany

Greece

Hungary

Ireland

Israel

Italy

Korea

Lithuania

Malaysia

Poland

Russia

Slovenia

South Korea

Spain

Sweden

Switzerland

Taiwan

Thailand

The Netherlands

Turkey

USA

United Kingdom

coun

trie

s

number of papers

accepted papers

submissions

Invited Talks

Frank van Harmelen (Vrije Universiteit, Amsterdam, Netherlands)

Ontology Mapping: A Way out of the Medical Tower of Babel?

Different approaches toontology matching

Linguistics & structure

Shared vocabulary

Instance-based matching

Shared background knowledge

Conclusions

Ontology mapping is (still) hard & open

Many different approaches will be required:linguistic,

structural

statistical

semantic

…

Currently no roadmap theory on what's good for which problems

Invited Talks

Paul Lukowicz (University for Health Sciences, Medical Informatics and Technology, UMIT Hall in. Tirol, Austria)

Human Computer Interaction in Context-Aware Wearable Systems

Wearable VisionWearable Vision

• non disruptive interaction • environment oriented

output environment oriented context recognition/monitoring

• physically unobtrusive technology systems

• seamlessly connected

wear.system

userreal

world100%

>>50% 100%

applications

What is Context Recognition ?What is Context Recognition ?

Embedded Controllers: feedback control loop

Artificial Intelligence: imitating human cognition and perception

- includes interpretation

Context Recognition: mapping signals from a group sensors onto a set of predefined, environment related states

Wearable VisionWearable Vision

wear.system

userreal

world100%

>>50% 100%

• wearable computer: system as an enhancer and facilitator in the interaction between the user and the real world

• a whole new field of applications• context recognition is the key issue

Temporal Representation and Reasoning

Decision Support Systems

Clinical Guidelines and Protocols

Ontology and Terminology

Case-Based Reasoning, Signal Interpretation, Visual Mining

Intelligent Image Processing

Knowledge Management

Machine Learning, Knowledge Discovery and Data Mining

Programme - Sessions

Temporal Rep. & Reasoning

TopicsTemporal Data Abstraction

Temporal Patterns, Probabilistic Methods

Learning Temporal Rules

Temporal Data ModelsFuzzy Temporal Framework

Bayesian-Network Models

Application Areas(N)ICUs: Artificial Ventilation, Blood Glucose

Hemodialysis Sessions, Gene Expression Data

Rules[Antecedent, Concequent]

Temporal RelationsTwo Episodes

PRECEDES: Allen‘s Temp. Operators

ApplicationsHemodialysis Sessions

Gene Expression Data

Learning Rules with Complex Temporal Patterns in Biomedical DomainsLucia Sacchi, Riccardo Bellazzi, Cristiana Larizza, Riccardo Porreca, Paolo Magni

Time series represented through complex TAs

I = [Increasing]D = [Decreasing]

S = [Steady]

Definition of a set of significant patterns

P = {p1, … , pN}

P1= [Increasing Decreasing]P2 =[Decreasing Increasing]

INPUT: raw data (biomedical time series)

APRIORI-like rule extraction algorithm

OUTPUT: set of temporal rules

TA mechanism

Time series represented through a set of basic trend TAs

a)

b)

c)

Support (Sup) = RTS / TS

Confidence (Conf ) = NARTS / NAT

Example: Hemodialysis Sessions Monitoring

Learning Rules with Complex Temporal Patterns in Biomedical DomainsLucia Sacchi, Riccardo Bellazzi, Cristiana Larizza, Riccardo Porreca, Paolo Magni

RULE (OPERATOR: PRECEDES) P={[Increasing Steady Decreasing], [Decreasing Steady Increasing]}

Antecedent Consequent

Variable Pattern Variable Pattern

Confidence Support

SP

DP [ISD]

[ISD] HR [DSI]

0.706 0.156

HR [DSI] DP [ISD] 0.755 0.398

HR [DSI] SP [ISD] 0.8 0.407

Point-based Semantic

ProblemsDownward

Inheritance

Upward Inheritance

Countability

A Three-Sorted Model:Interval-based Semantic

SolutionDownward

Inheritance

Upward Inheritance

Countability

Extending Temporal Databases to Deal withTelic/Atelic Medical Data Paolo Terenziani, Richard Snodgrass, Alessio Bottrighi, Mauro Torchio, Gianpaolo Molino

P_code Drug VT John Y 10:00,10:01,...,10:50,10:51,…,11:30 John Z {17:05, 17:06, …., 17:34} Mary Z 10:40,…,10:55,10:56,…,11:34 Ann Z 10:53, 10:34, …, 11:32

Data Models Grounded ...

P_code Drug VT John Y [10:00-10:50],[10:51,11:30] John Z {[17:05-17:34]} Mary Z [10:40-10:55],[10:56-11:34] Ann Z [10:53-11:32]

Ceilidh: Scottish Country Dancing

Clinical Guidelines & Protocols

TopicsDesign Patterns for Modelling CGPs

Information Extraction for Modelling CGPs

CGP Representation, Execution & Adaptation

Usability of CGPs

User Interfaces for CGP‘s Recommendations

Decision Theory for CGP’s Selection

CGP Retrieval – Concept Hierarchies

Quality Indicator for CGPs

Application AreasAsthma, Diabetes, Jaundice, (N)ICUs, Oncology, Otolaryngology

(CGP)

m

Architecture

Testing Asbru Guidelines and Protocols for Neonatal Intensive CareChristian Fuchsberger, Jim Hunter, Paul McCue

Data Abstraction

Execution Engine

Test Data

Visualisation

Guideline

Recommendations

IF O2 > O2-High THEN

Rec_FiO2 = FiO2 - 5

IF O2-High> O2 > O2-Low THEN

Rec_FiO2 = FiO2

IF O2-Low > O2 THEN

Rec_FiO2 = FiO2 + 10

O2-High

O2-Low

O2

FiO2 -

FiO2 +

8 kPa

6 kPa

Example: CGP: O2 Management

Results

The Problem

Gaining Process Information from Clinical Practice Guidelines Using Information ExtractionKatharina Kaiser, Cem Akkaya, Silvia Miksch

Knowledge-intensive

Cumbersome

Time-consuming

Automation

Structuring

Traceability

Information Extraction Process Information

Gaining Process Information from Clinical Practice Guidelines Using Information ExtractionKatharina Kaiser, Cem Akkaya, Silvia Miksch

Results

Task 1: Detecting relevant sentencesFiltering and segmentation moduleRecall: 76 % Precision: 97 %

Task 2: Extracting processesProcess extraction, merging & grouping modulesRecall: 94 % Precision: 84 %

Idea: Linguistic Patterns

Ontology-Driven Extraction of Linguistic Patterns for Modelling Clinical GuidelinesRadu Serban, Annette ten Teije, Frank van Harmelen, Mar Marcos, Cristina Polo-Conde

instance[radiotherapy, produces, skin_reactions] inst_of

template[med_action, effect_op, med_effect] covers

o_fragment(MedAction produces MedEffect)

MedAction MedEffectproduces

producesradiotherapy skin_reactions

Evaluation Linguistic PatternsBreast cancer GL (chapters 2-4)

Ontology-Driven Extraction of Linguistic Patterns for Modelling Clinical GuidelinesRadu Serban, Annette ten Teije, Frank van Harmelen, Mar Marcos, Cristina Polo-Conde

7 (28%)

7 (35%)

8 (20%)

#Modelledsentenceswith patterns

18 (72%)2591Ch. 4

16 (80%)20134Ch. 3

30 (73%)41130Ch. 2

#Modelledsentencesprocessed

#Sentences modelled by knowledge engineer

#Sentences processedautomatically

7 (28%)

7 (35%)

8 (20%)

#Modelledsentenceswith patterns

18 (72%)2591Ch. 4

16 (80%)20134Ch. 3

30 (73%)41130Ch. 2

#Modelledsentencesprocessed

#Sentences modelled by knowledge engineer

#Sentences processedautomatically

Audience …

Ontology and Terminology

TopicsDesign & Building a (Domain) Ontology

Ontology of Time & Situoids

Terminology Extraction from Text

Terminology Alignment

Population Ontology Using NLP & ML

Application AreasAllergens, Oncology, Surgical ICUs, ICUs, Tissue Microarrays

The Problem

Combining different patient registrationsTerminologies have to be mapped

Ontology mapping is hard problem in generalEspecially when terminologies contain no structure

SituationACM, OLVG: list of reasons for ICU admission

DICE: hierarchical knowledge describing the reasons for ICU admission

Using Lexical and Logical Methods for the Alignment of Medical TerminologiesMichel Klein, Zharko Aleksovski

I- atcherI- atcher

The Approach

aspect taxonomies

given

anatomicallocation

abnormality bodysystem

1. lexical methods

2. classificationaneurysma aortaacute pharyngitisaneurysma cordisaxillo- popliteale bypasscerebrovasculairaccidentcolon carninoom…

AIDSacute pancreatitisaneurysma van aortaarterial haemorrhage…tuberculeuzemeningitistumor …

structured ontology list of terms

Using Lexical and Logical Methods for the Alignment of Medical TerminologiesMichel Klein, Zharko Aleksovski

OLVG: Acute respiratory failureDICE: Asthma cardiale

OLVG: HIVDICE: AIDS

OLVG: Aorta thoracalis dissectie type B DICE: Dissection of artery

cause

location,abnormality

abnormality

Example Results

… still there!

Intelligent Image Processing

TopicsElectrocardiographic Imaging – Activation Maps

Cellular Neural Networks

Sketch Understanding

Automatic Segmentation

Support Vector Machines

Application AreasECG‘s Analysis, Cephalometric Analysis – X-rays

Anatomy, Bone Scintigraphy (Whole-body Bone Scan)

Idea: Anatomical Sketching

Sketching is ubiquitous in medicinePatient recordsCommunication with patientsConsultationMedical education

Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette

Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette

AnatomicalStructure & View

…Template 1 Template m

Naïve Bayes Classifier

Lung External

User Sketch

Template 1 Template m…

Shape ContextMatching

Recognition Process

Data CollectionCollected sample sketches from 3rd – 6th year medical students70 sketches of 6 anatomical structures, 2-3 views per structure: 1050 sketchesCompared: Student accuracy vs UNAS (UNderstaning Anatomical Sketches) accuracy

Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette

Evaluation: Accuracy

Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette

48.076.057.052.081.057.067.086.0UNAS

91.093.393.880.587.197.692.992.9Students

InternalExternalInteriorPosteriorAnteriorBasalSagittalParietal

LungHeartBrain

48.076.057.052.081.057.067.086.0UNAS

91.093.393.880.587.197.692.992.9Students

InternalExternalInteriorPosteriorAnteriorBasalSagittalParietal

LungHeartBrain

66.3067.052.067.076.052.081.076.0UNAS

93.8196.797.193.395.797.698.699.0Students

InternalExternalInternalExternalBasalParietalAnterior

Overall Accuracy

(%)

KidneyStomachSkull

66.3067.052.067.076.052.081.076.0UNAS

93.8196.797.193.395.797.698.699.0Students

InternalExternalInternalExternalBasalParietalAnterior

Overall Accuracy

(%)

KidneyStomachSkull

Baseline random classification accuracy = 6.7%

Conference Dinner

Knowledge Management

TopicsMulti-Agent Patient Representation

Clinical Reasoning Learning

Process Reengineering

Application AreasPrimary Care

Cognitive Processes during Clinical Reasoning

Cardiac Infarction Diagnosis

Hospital Logistic Processes

Machine Learning,Knowledge Discovery & Data Mining

TopicsWeb Mining

Clustering Methods: Similarities & Statistical Techniques

Naïve Bayesian, Rule-based, Case-based, Tree-based, and Genetic Algorithm, Inductive Logic Programming

Scenarios Learning

Subgroup Mining – User-Guided Refinement

Application AreasAcute Paediatric Abdominal Pain, Cardiac Monitoring, Corneal Disease, Dental Medicine (Prosthetic Appliance), Dietary Menu Planning, Epidemiologic Surveillance, Gene Expression Data, ICU, Influenza Sequences, MR Spectra, Oncology, Public Health Care

User-Guided ApproachSubgroup mining method=> potentially guilty (faulty!) elements

Visualization to ease interpretation

User has full control of the refinement process

Subgroup Mining for Interactive Knowledge RefinementMartin Atzmueller, Joachim Baumeister, Achim Hemsing, Ernst-Jürgen Richter, Frank Puppe

Visualization

Subgroup Mining for Interactive Knowledge RefinementMartin Atzmueller, Joachim Baumeister, Achim Hemsing, Ernst-Jürgen Richter, Frank Puppe

Conclusions

AIME is “healthy“128 % more submissions than last AIME-2003=> Decrease of acceptance rate (long: 23.6%; short: 22.3%)

General HighlightsDoctoral Consortium

3 Tutorials and 2 Workshops

Two excellent invited talks

Conclusions

Content HighlightsMedical application areas are very broad

‘Clinical Guidelines and Protocols’ has matured

‘Ontologies and Terminologies’ is a hot topic and generated a lot of discussion

Dealing with temporal data and information is crucial

Comparing the usefulness of different machine learning and mining techniques brings more insights

‘Intelligent Visualization’ is emerging in AIME

AIME 07AIME 07

Academic Medical CentreUniversity of Amsterdam

Local Organiser: Ameen Abu-HannaAmeen Abu-Hanna

will be hosted by:

Haste ye back

(Come back again soon!)

AIME 05AIME 05