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Programme Committee Chair Silvia Miksch, Vienna, Austria Organising Committee Chair

Artificial Intelligence in Medicine. Programme Committee Chair Silvia Miksch, Vienna, Austria Organising Committee Chair Jim Hunter, Aberdeen, UK. Where. AIME 05 was hosted by the Department of Computing Science, University of Aberdeen. Participants.

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Programme Committee Chair Silvia Miksch, Vienna, Austria Organising Committee Chair

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  1. Artificial IntelligenceinMedicine Programme Committee Chair Silvia Miksch, Vienna, Austria Organising Committee Chair Jim Hunter, Aberdeen, UK

  2. Where ... • AIME 05 • was hosted by the • Department of • Computing Science, • University of Aberdeen

  3. Participants 120 registrations for main conference 16 for pre-conference events Australia 2 Austria 9 Canada 5 Cyprus 1 Czech Rep. 1 Denmark 4 France 19 Germany 6 Greece 1 Hungary 1 Italy 15 Israel 1 Lithuania 2 Netherlands 12 Nigeria 2 Poland 1 Russia 1 Slovenia 3 South Korea 3 Spain 4 Sweden 3 Switzerland 2 Thailand 2 UK 13 USA 8 42% students

  4. New Event • Doctoral Consortium • Chair: Elpida Keravnou (Nicosia, Cyprus) • 8 PhD Students • Participating Faculty • Ameen 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)

  5. Tutorials • Evolutionary Computation Approaches to Mining Biomedical Data • John Holmes (University of Pennsylvania, USA) • Causal Discovery from Biomedical Data • Subramani Mani (University of Pittsburg, USA) • Applied Data Mining in Clinical Research • John Holmes (University of Pennsylvania, USA)

  6. Workshops • IDAMAP-2005: Intelligent Data Analysis in Medicine and Pharmacology • Niels Peek (University of Amsterdam, Netherlands)John Holmes (University of Pennsylvania, USA) • Biomedical Ontology Engineering • Jeremy Rogers, Alan Rector, Robert Stevens(University of Manchester, UK)

  7. Submissions and Reviewing 2005 – Reviewing Process PC Members: + 13 persons incl. 5 physcians additional reviewers approx. 3 reviews / paper 128 % more submissions

  8. Acceptance Rates 2005: 35 long 33 short AIMDM 99= AIME + ESMDM

  9. Accepted Papers by Country

  10. Invited Talks • Frank van Harmelen (Vrije Universiteit, Amsterdam, Netherlands)Ontology Mapping: A Way out of the Medical Tower of Babel?

  11. Different approaches toontology matching • Linguistics & structure • Shared vocabulary • Instance-based matching • Shared background knowledge

  12. 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

  13. 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

  14. real user 100% world > % > 5 0 0 0 1 % wear. system applications Wearable Vision • non disruptive interaction • environment oriented • output environment oriented • context recognition/monitoring • physically unobtrusive • technology • systems • seamlessly connected

  15. What is Context Recognition ? 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 Embedded Controllers: feedback control loop

  16. real user 100% world > % > 5 0 0 0 1 % wear. system Wearable Vision • 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

  17. Programme - Sessions • 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

  18. Temporal Rep. & Reasoning • Topics • Temporal 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

  19. Learning Rules with Complex Temporal Patterns in Biomedical DomainsLucia Sacchi, Riccardo Bellazzi, Cristiana Larizza, Riccardo Porreca, Paolo Magni INPUT: raw data (biomedical time series) a) TA mechanism Time series represented through a set of basic trend TAs I = [Increasing] D = [Decreasing] S = [Steady] b) Definition of a set of significant patterns P = {p1, … , pN} Time series represented through complex TAs P1= [Increasing Decreasing] P2 =[Decreasing Increasing] c) APRIORI-like rule extraction algorithm OUTPUT: set of temporal rules • Rules • [Antecedent, Concequent] • Temporal Relations • Two Episodes • PRECEDES: Allen‘s Temp. Operators • Applications • Hemodialysis Sessions • Gene Expression Data

  20. Learning Rules with Complex Temporal Patterns in Biomedical DomainsLucia Sacchi, Riccardo Bellazzi, Cristiana Larizza, Riccardo Porreca, Paolo Magni • Support (Sup) = RTS / TS • Confidence (Conf ) = NARTS / NAT • Example:Hemodialysis Sessions Monitoring

  21. Point-based Semantic Problems Downward Inheritance Upward Inheritance Countability A Three-Sorted Model:Interval-based Semantic Solution Downward Inheritance Upward Inheritance Countability Extending Temporal Databases to Deal withTelic/Atelic Medical DataPaolo Terenziani, Richard Snodgrass, Alessio Bottrighi, Mauro Torchio, Gianpaolo Molino Data Models Grounded ...

  22. Ceilidh: Scottish Country Dancing

  23. Clinical Guidelines & Protocols (CGP) • Topics • Design 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 Areas • Asthma, Diabetes, Jaundice, (N)ICUs, Oncology, Otolaryngology

  24. m Testing Asbru Guidelines and Protocols for Neonatal Intensive CareChristian Fuchsberger, Jim Hunter, Paul McCue Results Example: CGP: O2Management O2 IFO2 > O2-High THEN Rec_FiO2 = FiO2 - 5 IFO2-High> O2 > O2-Low THEN Rec_FiO2 = FiO2 IF O2-Low > O2THEN Rec_FiO2 = FiO2 + 10 FiO2 - 8 kPa O2-High O2-Low 6 kPa FiO2 + • Architecture Guideline Test Data Data Abstraction Execution Engine Recommendations Visualisation

  25. Gaining Process Information from Clinical Practice Guidelines Using Information ExtractionKatharina Kaiser, Cem Akkaya, Silvia Miksch • The Problem Knowledge-intensive Cumbersome Time-consuming Automation Structuring Traceability

  26. Gaining Process Information from Clinical Practice Guidelines Using Information ExtractionKatharina Kaiser, Cem Akkaya, Silvia Miksch • Information Extraction Process Information • Results • Task 1: Detecting relevant sentences • Filtering and segmentation module • Recall: 76 % Precision: 97 % • Task 2: Extracting processes • Process extraction, merging & grouping modules • Recall: 94 % Precision: 84 %

  27. Ontology-Driven Extraction of Linguistic Patterns for Modelling Clinical GuidelinesRadu Serban, Annette ten Teije, Frank van Harmelen, Mar Marcos, Cristina Polo-Conde MedAction MedEffect produces skin_reactions radiotherapy produces • Idea: Linguistic Patterns instance[radiotherapy, produces, skin_reactions] inst_of template[med_action, effect_op, med_effect] covers o_fragment(MedAction produces MedEffect)

  28. Ontology-Driven Extraction of Linguistic Patterns for Modelling Clinical GuidelinesRadu Serban, Annette ten Teije, Frank van Harmelen, Mar Marcos, Cristina Polo-Conde • Evaluation Linguistic Patterns • Breast cancer GL (chapters 2-4)

  29. Audience …

  30. Ontology and Terminology • Topics • Design & Building a (Domain) Ontology • Ontology of Time & Situoids • Terminology Extraction from Text • Terminology Alignment • Population Ontology Using NLP & ML • Application Areas • Allergens, Oncology, Surgical ICUs, ICUs, Tissue Microarrays

  31. Using Lexical and Logical Methods for the Alignment of Medical TerminologiesMichel Klein, Zharko Aleksovski • The Problem • Combining different patient registrations • Terminologies have to be mapped • Ontology mapping is hard problem in general • Especially when terminologies contain no structure • Situation • ACM, OLVG: list of reasons for ICU admission • DICE: hierarchical knowledge describing the reasons for ICU admission

  32. Using Lexical and Logical Methods for the Alignment of Medical TerminologiesMichel Klein, Zharko Aleksovski cause abnormality Example Results 1. lexical methods • OLVG: Acute respiratory failureDICE: Asthma cardiale • OLVG: HIVDICE: AIDS • OLVG: Aorta thoracalis dissectie type B DICE: Dissection of artery 2. classification location,abnormality • The Approach anatomical abnormality body location system aspect taxonomies given structured ontology list of terms AIDS aneurysma aorta acute pancreatitis acute pharyngitis aneurysma van aorta aneurysma cordis arterial haemorrhage axillo - popliteale bypass … cerebrovasculair tuberculeuze meningitis accident tumor colon carninoom … …

  33. … still there!

  34. Intelligent Image Processing • Topics • Electrocardiographic Imaging – Activation Maps • Cellular Neural Networks • Sketch Understanding • Automatic Segmentation • Support Vector Machines • Application Areas • ECG‘s Analysis, Cephalometric Analysis – X-rays • Anatomy, Bone Scintigraphy (Whole-body Bone Scan)

  35. Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette • Idea: Anatomical Sketching • Sketching is ubiquitous in medicine • Patient records • Communication with patients • Consultation • Medical education

  36. Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette Template 1 Template m Recognition Process User Sketch Naïve Bayes Classifier Lung External Shape Context Matching Anatomical Structure & View … … Template m Template 1

  37. Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette • Data Collection • Collected sample sketches from 3rd – 6th year medical students • 70 sketches of 6 anatomical structures, 2-3 views per structure: 1050 sketches • Compared: Student accuracy vs UNAS (UNderstaning Anatomical Sketches) accuracy

  38. Anatomical Sketch Understanding: Recognizing Explicit and Implicit StructurePeter Haddawy, Matthew Dailey, Ploen Kaewruen, Natapope Sarakhette • Evaluation: Accuracy Baseline random classification accuracy = 6.7%

  39. Conference Dinner

  40. Knowledge Management • Topics • Multi-Agent Patient Representation • Clinical Reasoning Learning • Process Reengineering • Application Areas • Primary Care • Cognitive Processes during Clinical Reasoning • Cardiac Infarction Diagnosis • Hospital Logistic Processes

  41. Machine Learning,Knowledge Discovery & Data Mining • Topics • Web 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 Areas • Acute 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

  42. Subgroup Mining for Interactive Knowledge RefinementMartin Atzmueller, Joachim Baumeister, Achim Hemsing, Ernst-Jürgen Richter, Frank Puppe • User-Guided Approach • Subgroup mining method=> potentially guilty (faulty!) elements • Visualization to ease interpretation • User has full control of the refinement process

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

  44. Conclusions • AIME is “healthy“ • 128 % more submissions than last AIME-2003=> Decrease of acceptance rate (long: 23.6%; short: 22.3%) • General Highlights • Doctoral Consortium • 3 Tutorials and 2 Workshops • Two excellent invited talks

  45. Conclusions • Content Highlights • Medical 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

  46. AIME 07 will be hosted by: Academic Medical Centre University of Amsterdam Local Organiser: Ameen Abu-Hanna

  47. AIME 05 Haste ye back (Come back again soon!)

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