Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax

# Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax

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## Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax

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1. Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax Using an ARDS detection automaton as a working example Jeroen S. DE BRUIN1,2,Heinz STELTZER3, Andrea RAPPELSBERGER1, and Klaus-Peter ADLASSNIG1,2 1 Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria; 2 Medexter Healthcare, Borschkegasse 7/5, 1090 Vienna, Austria; 3 Trauma Hospital Vienna South, Kundratstrasse 37, 1120 Vienna, Austria AMIA 2017 Annual Symposium — November 04‒08, 2017 Washington DC

2. Medical conditions as automata With certain chronic or progressive medical conditions (e.g., acute respiratory distress syndrome, or ARDS), it is intuitive to model a disease’s progression in terms of states States, or state names, are medical, semantically rich, linguistic concepts As the patient gets better or worse, he moves between disease states. This dynamic behavior of health can be modeled by transitions. Benefits Intuitive, easy to understand, easier to visualize Drawbacks States are too rigid, often mutually exclusive, model is oftenoversimplified

3. Linguistic uncertainty In medicine, linguistic concepts used for classification of diseases, symptoms or syndromes are inherently unsharp with respect their boundaries Fuzzy sets and logic can be used to reflect this linguistic uncertainty Fuzzy sets express the relationship between linguistic terms and measured or observed data as a degree of compatibility Degree is calculated by a membership function Fuzzy logic models propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts Approximate reasoning instead of exact rule inference

4. Fuzzy automata A system (patient) can be in more than one state at the same time Membership of each state is expressed as a degree of compatibility Combinations of state values make the model more expressive Multiple simultaneous state transitions possible Transitions between linguistic concepts more gradual and thus more intuitive Result: Fuzzy automata well-suited for the use in (automated) clinical monitors Evaluation and interpretation of streams of patient data in brief time intervals Reduction of dimensionality of input data Presentation of outcomes in semantically meaningful, clinically relevant linguistic concepts

5. Standard for medical knowledge representation: Arden Syntax A standard language for writing situation-action rules, procedures, or knowledge bases that trigger results based on clinical events detected in patient data Each module, referred to as a medical logic module (MLM), contains sufficient knowledge to make at least a single medical decision Extended by medical knowledge packages (MKPs) consisting of interconnected MLMs for complex clinical decision support Since version 2.9 – formal support for constructs based on fuzzy set theory and fuzzy logic Healthcare industry and academic users

6. Fuzzy Arden Syntax Purpose Introduce fuzziness into clinical decision making (as a virtue not as a deficiency!) Main concepts Extension of the truth value model, defining a truth value over a continuous spectrum in a range [0, 1] rather than a dichotomous “true/false” or “yes/no”, resp., model Introduction of the fuzzy set data type to model the unsharpness of boundaries in definitions of linguistic concepts Introduction of three basic propositional fuzzy logic operations– conjunction, disjunction, and negation – which are equipped to handle all truth values in the specified range [0, 1] Introduction of parallel, weighted program branches to handle conditional statements where the condition is neither true nor false

7. Fuzzy Arden Syntax: An example Native support for fuzzy set declaration Native support for compatibility calculation

8. FuzzyArden ARDS Knowledge-based decision support monitoring patients with acute respiratory distress syndrome (ARDS) early detection of ARDS therapy advice in ARDS cases International study (Vienna, Berlin, Marburg, Paris, Milan) to improve ARDS definition to compare therapy entry criteria

9. FuzzyArden ARDS automaton start normal hypoxic improved after hand bagging not improved after hand bagging not responding to high FiO2 responding to high FiO2

10. FuzzyArden ARDS fuzzy sets

11. FuzzyArden ARDS fuzzy set definition in Arden Syntax 3D fuzzy sets with the same pre- and postconditions can be directly defined 3D fuzzy sets with different pre- and postconditions require explicit definition of both

12. FuzzyArden ARDS fuzzy set definition in Arden Syntax Duration modeling natively supported by Arden Syntax 3D fuzzy set will yield a degree of compatibility equal to the maximum of all calculations (sup-min composition)

13. FuzzyArden ARDS linguistic state transitions

14. FuzzyArden ARDS state transitions in Fuzzy Arden Syntax Creating enumeration objects for better readability

15. FuzzyArden ARDS state transitions in Fuzzy Arden Syntax Explicit indication a value is a truth value, to trigger calculation using fuzzy logic (fuzzy and, fuzzy or) Using the sup-min composition again to determine the new degree of compatibility of the “normal” state

16. Discussion Fuzzy Arden Syntax for fuzzy automata Rules closely resemble natural language, clinicians can verify the implemented knowledge in MLMs easily without in-depth knowledge of modern programming languages Presentation of automaton configuration in medical, semantically rich, linguistic concepts, thus easier interpretable by clinicians Native support for fuzzy sets and fuzzy logic in natural language concepts, improves readability Limitations Representation of 3D fuzzy sets not (yet) intuitive No prospective tests so far, thus real-time performance is still unknown