Medizinische Experten- und wissensbasierte Systeme

2. Objectives Support of medical decision making: for the single patient provide a correct diagnosis selection of an optimal therapy correct assessment of prognosis optimal patient`s management in medical institution

3. Knowledge-Based Methodology knowledge level modelling of mental processes; linguistically communicated modelling based on symbols (linguistic concepts = abstract concepts) objective and subjective knowledge (definitional, causal, statistical, and heuristic knowledge) measurements and observational level measured and observed data data-to-symbol conversion

9. Fuzziness in Medicine vagueness of medical concepts gradual transition from one concept to another uncertainty of medical conclusions uncertainty of co-occurrence of vague medical concepts incompleteness of medical data and medical theory partially known data and partially known explanations of medical phenomena

10. FuzzyKBWean:A Fuzzy Control System forWeaning from Artificial Ventilation C. Schuh, M. Hiesmayr, K.-P. Adlassnig Department of Medical Computer Sciences Department of Cardiothoracic and Vascular Anaesthesia and Intensive Care University of Vienna Medical School and Vienna General Hospital

11. Objective mechanically ventilated patients after cardiothoracic surgery in an Intensive Care Unit (ICU) proposals for changes of the ventilator settings during the three phases of mechanical ventilation (stabilization, weaning, and finally extubation of the patient) open-loop system: integration into the patient data management system (PDMS) ; time resolution: 1 minute closed-loop system as a long-term objective: integration into the ventilator (auto-mode)

14. Structure of FuzzyKBWean

15. Methods phase-dependent fuzzy sets linguistic If/Then rules If: patient’s physiological parameters and ventilator measurement parameters (in a defined context) Then: proposals for changes of ventilator settings fuzzification step arithmetic, statistical, comparative, logical, temporal, and control operators defuzzification step center of gravity method verification by the attending physician, i.e., open-loop

16. Ed27.gifEd27.gif

18. Fuzzy Control

19. Knowledge Base

20. PATIENT:

23. Results – 1 23 variables 74 fuzzy sets (phase-dependent) 16 If/Then rules 4 rules checking for measurement errors and validity 3 rules for ventilation (normal range, hypoventilation, hyperventilation) 4 rules for oxygenation (stabilization, oxygenation normal, hypoxia, severe hypoxia) 4 rules for intermediate states (increased EtCO2, decreased EtCO2, phase changes) 1 rule for extubation

24. Weaning Rule Wean_1 If mean EtCO2 during the last 30 minutes is contained in fuzzy set EtCO2_wean_normal and if rule Wean_1 has not been activated during the last 30 minutes and if EtCO2 is valid and if EtCO2 is normal Then PIP -3

25. Results – 2 10 prospectively randomized patients FuzzyKBWean reacted correctly 131 (SEM 47) minutes earlier than the attending physician adjustment of ventilation parameters was more reliable than adjustment of oxygenation (EtCO2 is more reliable as SpO2) phase-specific rules often proposed too small changes of the ventilator settings temporal rule blocking, fuzzy set adaptations, employing thresholds to avoid oscillations

26. Results – 3Delay of Staff Reaction in Case of Hyperventilation

27. Discussion methodology minimal number of therapeutically significant classes per variable gradual transition between variable classes adequat consideration of inherent vagueness of medical concepts intuitive If/Then rules on the knowledge level physician`s medical knowledge was transfered to FuzzyKBWean clinical trial periods of deviation from the target parameters are shorter contribution to patient`s safety and comfort closed-loop: recognition of artifacts and information obtained by direct observation of the patient

28. CADIAG-II:A Hospital-Based Consultation System forInternal Medicine Klaus-Peter Adlassnig, G. Kolarz Department of Medical Computer Sciences University of Vienna Medical School

29. Objectives diagnostic hypotheses, confirmed and excluded diagnoses indication of rare diseases proposals for further examinations ranked according to invasiveness and costliness pathological findings not yet accounted for search for further diagnoses correct and complete differential diagnoses at minimal risk for the patient and costs for the health care system

31. CADIAG-II: Methods patient data symptom, signs, and test results are modelled as context dependent fuzzy sets diseases or diagnoses takes values in [0,1] knowledge representation symptom and disease hierarchies crisp rule and fuzzy set based data-to-symbol conversion symptom/disease, symptom/symptom, disease/disease relationships, and complex diagnostic rules with frequency of occurrence and strength of confirmation inference mechanism manifold application of the compositional rule of fuzzy inference

38. Examples Example 1 (indicating): IF elevated amylase level in serum THEN acute pancreatitis WITH (?O = very often [?O = 0.90], ?B = strong [?C = 0.70]). Example 2 (necessary and sufficient): IF rheumatoid arthritis and splenomegaly and leukopenia less than 4 giga/l THEN Felty’s Syndrom WITH (?O = always [?O = 1.00], ?C = confirming [?C = 1.00]).

40. Inference Mechanism

51. Results Rheumatology more than 200 disease profiles, more than 2.000 findings more than 50.000 finding-disease-relationships more than 160 complex rules Hepatology and Gastroenterology more than 100 disease profiles, more than 1.000 findings more than 30.000 symptom-disease-relationships more than 40 complex rules

52. Evaluation

53. MedFrame/CADIAG-IV:A Consultation System Framework for Internal Medicine and Related Areas Klaus-Peter Adlassnig Department of Medical Computer Sciences University of Vienna Medical School

54. Objectives CADIAG-IV positive and negative diagnostic hypotheses, confirmed and excluded diagnoses positive and negative therapy proposals, necessary and excluded therapies MedFrame shell for medical knowledge-based systems knowledge-based telemedicine service

55. MedFrame/CADIAG-IV:Methods extension of CADIAG-II: symptoms, diseases, and therapies context-sensitive data-to-symbol conversion and patient specific adaptation of the knowledge base extended knowledge representation with step-by-step knowledge acquisition refinement MedFrame client/server architecture, WWW compatible integrated patient data and medical knowledge base

56. MedFrame Structure

59. Symptom-Disease Relationships

61. MedFrame/CADIAG-IV:Discussion mainframe CADIAG-II to Medframe/CADIAG-IV integrated patient data and medical knowledge base patient data and knowledge transfer theoretical extensions extension of the relationship, rule, and inference concept medical extensions rheumatology, hepatology, gastroenterology, radiology, neurology HEPAXPERT, TOXOPERT in MedFrame

62. Conclusions knowledge-based systems are becoming part of medical practice computational intelligence in medicine vagueness, uncertainty, and incompleteness of medical data and medical knowledge demand a flexible and extended formal framework medical knowledge representation and inference fuzzy set theory and fuzzy logic provide an appropriate solution