Rainer Unland

1. Multi-Agent Systems and Soft Computing Technologies: A promising symbiosis for reliable medical diagnosis Rainer Unland

2. Project partner • Common project with • Prof. Jürgen Klüver (Department of Sociology) • Dr. Christina Stoica (Department of Sociology) • How to apply soft computing techniques in communication and social sciences • Project is still in its early stage • More ideas than solutions

3. Medical Diagnosis • Medical diagnosis generation belongs to the category of highly complex and critical tasks • Huge number of factors by which a disease may be characterized • Some characteristics may not show up / may be overshadowed while others (new ones) may come along • Characteristics may come in different forms/appearances and can be more or less obvious/strong

4. Medical Diagnosis • Difficult medical cases • Patient’s symptoms do not sufficiently match the typical disease patterns of known diseases • Patient suffers from a combination of diseases • Patient, although (s)he suffers from a specific disease, does not exhibit the typical symptoms of this disease • Disease is not yet known; i.e., is new • Disease usually occurs in specific regions of the world only; the place where the patient is treated right now is none of these regions • The last two cases usually mean that the symptoms can not be interpreted in the right way

5. Medical Diagnosis • Disease: • Described by symptoms on different levels: • Key symptoms (mandatory) • Secondary symptoms (contingent, however, likely) • Additional symptoms (may occur, however, are less likely)

6. Medical Diagnosis • Key symptoms often point to more than one possible disease • Establishing a diagnosis is fundamentally a process of finding evidence to distinguish a probable cause of the patient's key symptoms from all other possible causes of the symptom • This process is called differential diagnosis • Differential diagnosis : • Start with key symptoms and identify “first choice” of disease • Working hypothesis • Describes tentative goal • Gather evidence in support of working hypothesis • History (patient file) • Physical examinations • Tests, clinical, radiological, and laboratory data, etc.

7. Medical Diagnosis • Common in computer aided medical diagnosis are • Expert systems (ES); e.g. MYCIN-type • Disadvantages of ES: • Powerful ES come comprise a huge number of (ad hoc) rules, which make it difficult to improve them in case of medical progress/learning experience • Expert systems are not good at recognizing when no answer exists or when the problem is outside of their area of expertise • In addition to a great deal of technical knowledge, human experts have common sense. It is not yet known how to give expert systems common sense.

8. Medical Diagnosis • Common in computer aided medical diagnosis are • Certain artificial neural networks, mainly multi-layered perceptrons (PS) • Disadvantages of PS: • Difficulties in determining the best architecture • Necessity to start training all over again if new diseases and symptoms are to be introduced • "Black box" nature • Proneness to overfitting

9. Architecture of the Holonic Medical Diagnosis System • Idea: Construction of a Holonic Medical Diagnosis System that relies on • Multi-agent system technology • Holonic architecture • From one big monolithic systems to a flexible, fault tolerant and reliable system that consists of a huge number of simple components that, nevertheless, are able to cooperate to solve complex problems • The holonic entities (holons) are realized by agents that inherit many of the key properties of holonic systems • Self-sustainability, cooperation, self-organization, flexibility, robustness, and re-configurability • Soft-computing technologies • Keep human beings (experts) as muchas possible out of the loop

10. Architecture of the Holonic Medical Diagnosis System • Holonic medical diagnosis system • System of collaborative medical entities and experts that cooperate in order to come to a sound medical diagnosis in case of an uncertain situation (from the point of view of the responsible physician(s)). • Diagnosis holarchy exposes a tree-like structure • Each inner node is an expert on a specific domain of diseases • Higher levels cover broader domains on a more general level • Lower levels are more specialized, however, also cover a less comprehensive domain of diseases. • Each leaf of the tree represents an expert on a specific disease

11. connection to the outside world, Internet, requester Holonic Medical Diagnosis System (HMDS) Mediator agent DSA DSA DSA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA Architecture of the Holonic Medical Diagnosis System Holonic hierarchy • Basic Resource Level: Disease Representative Agent (DRA) • Intra-Organizational Level: Diagnosis Specialist Agent (DSA) • Inter-OrganizationalLevel: Global Collaboration Level

12. Architecture of the Holonic Medical Diagnosis System • Most important layer of the holarchy is the intra-organizational layer. • It consists of set of holons/agents that are arranged in a tree-like structure • Interface of this layer to the outside is realized by a special agent, the mediator agent • Represents the only gateway to the HMDS; i.e., all communication to and from the outside world is done via this mediator agent

13. Architecture of the Holonic Medical Diagnosis System • Diagnosis Specialist Agent (DSA) • Each DSA represents a specialist in a specific domain of related diseases • On a higher level, a DSA is a representative of a superset of the set of diseases that is represented by the next lower DSAs • DSAs can realize different doctrines or different views of a disease expert (e.g., traditional vs. homeopathic doctrine) or simply rely on different soft computing techniques • If it gets a request for a diagnosis, it interprets the CRPP in order to decide to what other DSA(s) or DRAs the CRPP is to be sent for further processing • After the DRAs have returned their diagnosis it interprets, compares, and evaluates them in order to define a ranking of likely diagnoses among all the diagnoses returned from different DRAs or DSAs

14. Architecture of the Holonic Medical Diagnosis System • Disease Representative Agent (DRA) • Representative of and an expert on a specific disease • Maintains a pattern store/database that contains disease description patterns (DDP) for all possible/known variants/ appearances of the disease • Maintains additional knowledge to be able to • interpret the computer readable patient pattern (CRPP) • relate it to its DDPs • Some relevant data/examinations may be missing • Compromises significance of other characteristics of CRPP • DRA needs to have deliberative capabilities to deal with such cases

15. Architecture of the Holonic Medical Diagnosis System • Key component of a deliberative agent is a central reasoning system • Constitutes the intelligence of the agent • Deliberative agents generate plans to accomplish their goals based on general knowledge they are equipped with (World model) • In accordance to these requirements, every DRA possesses an internal representation of the disease it represents (“disease model”) • allows it to interpret the CRPP and compare it to its DDPs • Results are interpreted and a probability value for the occurrence of the disease as well as an estimation of the reliability of the diagnosis (acatalepsia) are calculated • agent enhances its result with suggestions on how it can be improved/verified if not all information were available • if likelihood of disease is above a given threshold proposals for the treatment and information about disease are added (explanation)

16. Architecture of the Holonic Medical Diagnosis System • DRA relies on a hybrid architecture • Set of patterns that describe all known appearances of the disease • Knowledge base and inference engine that characterize the disease and places it in context • requires that the DRA is equipped with an extended disease model that allows it to interpret and interrelate the characteristics of the CRPP and to draw reliable conclusions with respect to the likelihood of the occurrence of the disease

17. Architecture of the Holonic Medical Diagnosis System • Results of the analysis • The most likely diagnosis (based on the evaluation on a comprehensive set of rules) • Probability value for the occurrence of the disease • Estimation of the reliability of the diagnosis

18. connection to the outside world, Internet, requester Holonic Medical Diagnosis System (HMDS) Mediator agent DSA DSA DSA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA From a Working Hypothesis to a Stable Diagnosis Requester request

19. connection to the outside world, Internet, requester Holonic Medical Diagnosis System (HMDS) Mediator agent DSA DSA DSA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA From a Working Hypothesis to a Stable Diagnosis Requester request request

20. connection to the outside world, Internet, requester Holonic Medical Diagnosis System (HMDS) Mediator agent DSA DSA DSA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA From a Working Hypothesis to a Stable Diagnosis Requester request request

21. connection to the outside world, Internet, requester Holonic Medical Diagnosis System (HMDS) Mediator agent DSA DSA DSA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA From a Working Hypothesis to a Stable Diagnosis Requester answer answer

22. connection to the outside world, Internet, requester Holonic Medical Diagnosis System (HMDS) Mediator agent DSA DSA DSA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA From a Working Hypothesis to a Stable Diagnosis Requester answer answer

23. connection to the outside world, Internet, requester Holonic Medical Diagnosis System (HMDS) Mediator agent DSA DSA DSA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA From a Working Hypothesis to a Stable Diagnosis Requester answer

24. Interactive Net • Higher levels of the hierarchy need to work on a broader and, thus, more abstract level: • They decide about categories • Way down • Pattern matching • More abstract: • In the order: • (Some) key symptoms are only considered • Secondary symptoms are considered as well • Need to be as open as possible • Idea is from the symptoms to the disease (not the other way round as with a working hypothesis)

25. Interactive Net • Static structure: • Structure of (participants) and, thus, the workflow within a holon is "predefined" • Higher level decides on which lower level needs to be involved (workflow specification) • Dynamic structure • Workflow (participants) is dynamically decided on the fly: • No predefined structure • E.g., Blackboard architecture • Much more flexible; whoever wants can contribute to the solution process

26. Interactive Net • Higher levels of the hierarchy need to work on a broader and, thus, more abstract level: • Way up • Comparison and assessment of results gotten from different subordinated agents • Decides on the most feasible hypothesis, treatment, or further investigations (if hypothesis is not solid enough) • May also come to the conclusion that non of the delivered hypothesis is "solid" enough • New request sent to different agents or "resignation" • Needs a lot of knowledge/understanding • Adapted real world model (medical model)

27. Architecture of the Holonic Medical Diagnosis System • Diagnoses may be on different levels (1) • In the best case, the information that was provided to the DRA, especially the CRPP, provides all the data and information that is needed in order to conduct a comprehensive match with the patterns of the pattern base • Results achieved by the DRA are optimal from its point of view and status, i.e., cannot be improved by any further information or data • Computed parameters for the probability of the disease and the reliability of the diagnosis are highly reliable and stable

28. Architecture of the Holonic Medical Diagnosis System • Diagnoses may be on different levels (2) • For other diagnoses, the CRPP may have provided only partial information. This influences the significance of the prediction of the appropriate DRA • In case the already available information in the CRPP does not exclude this disease (membership value/probability value is below a given threshold) the result of its analysis comes with a set of tasks/examinations that are suggested to be performed by the physician in order to further verify (or invalidate) the diagnosis • Clearly requires a deliberative agent architecture with the DSA exhibiting a profound and comprehensive understanding of the domain of disease it represents • Still a huge challenge!!

29. Architecture of the Holonic Medical Diagnosis System • Boundaries-Problem: • Paths/Holonic structure in the center of expertise may be very detailed/fine-grained • Like an aunt colony where the main stream (main route) is much more used than others; however, others nevertheless exist and hint to alternatives • The closer we come to boundaries the coarser/less detailed the holonic structure may become (shorter paths) • Nevertheless may deliver reliable results and hints that another disease (area) agent may be consulted

30. Alternative Models 1: Interactive Net • Candidates: • Interactive neural nets (IN) • Recurrent networks that are usually not trained • User has to construct the weight matrix him-/herself according to his/her particular problems • Proper weight matrices can be generated by optimization algorithms, e.g. genetic algorithms • There may be several hypotheses • No stable hypothesis (oscillating network) • Problem is still: explanation • Idea: Implement different angles IN

31. Alternative Models 2: Kohonen Feature Map or Self Organizing Map • Alternative Model (Explanation Component): • Kohonen Feature Map or Self Organizing Map (SOM) • In contrast to perceptrons SOMs belong to the type of non supervised learning networks • They operate according to the learning rule "winner takes it all", i.e., only neurons with the highest activation values pass their activation on to other neurons • The activation function is mainly a sigmoid function • The result of training processes of SOMs is a clustering of information

32. Alternative Models 2: Kohonen Feature Map or Self Organizing Map • There exist several types of SOMs • Ritter-Kohonen SOM type seems to be especially suitable for our problem • Data is ordered according to a "semantic matrix" • Contains two matrices: • Variable weight matrix like usual learning neural networks • Semantic matrix that must be constructed by the user KM

33. Communication • SOM and the IN are rather simple types • Their capabilities cannot be compared with advanced ES like the later MYCIN-types • Aim is to demonstrate that computer aided medical diagnosis can be done with rather simple systems • INs can easily be extended by adding new symptoms and diseases • Even the construction of the semantic matrix of the SOM or the weight matrix of the IN can be automatically done by coupling the SOM and the IN with supervised learning nets and/or evolutionary algorithms • The resulting "hybrid systems" can literally generate medical expert systems for every desirable level of complexity, thus, serves the purpose of our architecture to built complex and easily extendable and trainable systems from simple components

34. Communication • In case of non resolvable conflicts: • Negotiation (communication) needs to take place • First step: automatically (on the level of agents) • Closed environment (repetition of same type of HMDSs): • Assumption: Data is structured in the same way; communication partners work on the same basis (communication protocol, ontologies, etc.)

35. connection to the outside world, Internet, requester connection to the outside world, Internet, requester (HMDS) (HMDS) Mediator agent Mediator agent DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DSA DSA DSA DSA DSA DSA DSA DSA DSA DSA DSA DSA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA DRA From a Working Hypothesis to a Stable Diagnosis

36. Communication • In case of non resolvable conflicts: • Negotiation (communication) needs to take place • First step: automatically (on the level of agents) • Open environment: See Lotfi Zadeh's recent work about "semantic questions" on the Web and tomorrow • Vision!!!! • Second step: by also involving human experts CM

37. Conclusion • Idea with HMDS is to construct a "Medical Diagnosis Shell" with • High learning capabilities • High level of automation • Human beings are only consulted if unavoidable • System constantly improves itself • High scalability • Implementation Basis • FIPA Agent platform (Jade)