programme committee chair silvia miksch, vienna, austria organising committee chair jim hunter,...
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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
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AIME 05AIME 05