An architecture for intelligent support of authoring and tutoring in multimedia learning environments Alexander Seitz Dept. Of Artificial Intelligence, University of Ulm D-89069 Ulm, Germany email:
[email protected] Alke Martens, Jochen Bernauer Dept. For Medical Documentation and Computer Science, University of Applied Science Ulm D-89075 Ulm, Germany email:
[email protected] Claudia Scheuerer Central Institute for Biomedical Engineering, University of Ulm D-89069 Ulm, Germany Jens Thomsen Department of Medical Microbiology and Hygiene, University Hospital of Ulm D-89081 Ulm, Germany
Abstract: Both representing cases as fixed scripts as well as the development of initial expert systems as a basis for computer based tutoring systems is particularly difficult and time intensive in a multi-institutional development project. On the other hand, structured systematic knowledge and a common terminology is necessary for automatic generation of information and quiz pages as well as automatic feedback. Therefore we provide an architecture that relates tutoring cases to general concept representation systems containing common terminologies, and facilitates an incremental acquisition of systematic knowledge.
Introduction When analyzing the problem solving methods of medics, experts combine the application of specialized domain knowledge with a case-based approach to find the right diagnosis and therapy (Patel et al. 1995). Therefore, the education of students of medicine should include both the imparting of systematic knowledge together with caseoriented learning. This applies also to computer based training systems. Tutoring systems range from a hard coded representation of cases (Mayo Clinic 1996) to applications based on expert systems, where cases form an instantiation of pre-coded systematic knowledge (Schewe et al. 1996). In the former, the author has to develop fixed scripts of the tutoring process and gets no knowledge-based support for authoring and tutoring. The latter support the author by automatic generation of quiz and information pages and make an automatic feedback in the tutoring process possible. If clinical institutions of different domains have to work on such an expert system component, an initial complete development is difficult and time expensive. On the other hand, an incremental “case by case” coding by different authors requires a common terminology and a framework identifying and relating the central, medical concepts. We developed an architecture that contains terminological groups from different concept representation systems. It allows the construction of systematic knowledge by linking items of these groups together when authors build tutoring cases. As a result of this process we obtain a medical knowledge base that comprises all knowledge acquired case by case. Elements of this knowledge base are integrated with the authoring and tutoring system into a learning environment. The basic parts of both systems are complemented by components that make use of the knowledge base to support authoring and tutoring processes (Fig. 1).
Student
Tutoring System
Author
Authoring System
Tutoring Process
Case Knowledge Base
Relations Concept Representations
Figure 1: Architecture of the learning environment.
Identification of Central Concepts The concepts a medic usually uses to describe a medical case can be divided into the following topics: anatomy, phenomena which are located at a specific anatomy (examples for the term ‘phenomenon’ are ‘inflammation’, ‘pain’...), methods the medic uses to look for or to validate certain phenomena (examinations, technical examination, anamnesis) , diagnoses, and therapies. In our architecture, we reflect the medics’ perspective by representing the according terminological groups in different hierarchies and sets: •
the hierarchy of anatomic structures and body systems,
•
the set of phenomena,
•
the hierarchy of what we call methods, which comprises different (technical and non-technical) examinations and the anamnesis,
•
the hierarchy of diagnoses and
•
the hierarchy of therapies.
Contrary to the elements in the concept hierarchies, phenomena are structured objects consisting of a phenomenon name and a set of properties. For example, some of the properties the phenomenon ‘pain’ could have, are ‘intensity’, ‘duration’ or ‘quality’. In medical technical terminology, there exists a large range of systems for concept representation: classifications, nomenclatures or coding schemes. By separating the different hierarchies mentioned above, we are able to make use of existing terminology systems like special axes or parts of classifications, nomenclatures or special coding schemes. As an example, we utilize the "anatomy" axis of MeSH (Medical Subject Headings) (WHO 1992) and the ICD (Systematic Nomenclature of Medicine) (NLM 1999) diagnosis hierarchy.
Relating the Concepts The information inherent in medical tutoring cases is mainly represented by links between concepts, which are given as hierarchy items and phenomenon instances. For example, we establish links that relate phenomena at a certain anatomic structure to examination methods they are based upon, and to diagnoses and therapies they imply (Fig. 2). Concrete cases constitute instances of this network.
Phenomena
Diagnoses
Examinations
Therapies
Anatomy
Figure 2: Integration of phenomena into concept graphs. As an example, a tutoring case may describe a patient with anginose complaints. Because of this, the medic may order an electrocardiogram examination, which reveals a ST-segment depression. Taking other observed facts into consideration, the student’s diagnosis should be myocardial infarction. One necessary therapy for this disease is intensive care. When the author describes this case, several links are established. The phenomenon ‘ST-segment depression’ is connected with the anatomic structure ‘heart’. Their combination is linked both to the associated examination ‘electrocardiogram’ and the diagnosis ‘myocardial infarction’. Finally the diagnosis ‘myocardial infarction’ is linked to the therapy ‘intensive care’. By following explicit links we obtain further implicit ones. For example, a relation between ‘electrocardiogram’ and ‘myocardial infarction’ results from their connections to the given anatomy and phenomenon combination.
Supporting the Authoring Process We can use the hierarchies of anatomy, examination methods, diagnoses and therapies to support the author in formulating elements of tutoring cases. In (Fig. 3) an example of a tree-control is shown that offers a part of the MeSH anatomy structure for defining the location of a phenomenon. This can be done by selecting the appropriate concepts from the tree, which are inserted into the structured representation of the phenomenon on the right hand side of the depicted window. When building a case for tutoring, the author describes a number of phenomena observed at the patient. Furthermore he is able to relate phenomena at a certain location to one or more differential diagnoses. Offering to him links from already constructed tutoring cases that provide differential diagnoses for the observed phenomena can support this process. Structured medical case knowledge is also useful for building quiz pages by integrating the set or a subset of those differential diagnoses plus some other irrelevant ones into the pages. The aim of these quiz pages is that the student specifies correct differential diagnoses at different stages of the patient information gathering process. The final diagnosis is put together with other differential diagnoses, for which not sufficient evidence exists, to build a quiz page to find the final diagnosis, as it is shown in (Fig. 4). Furthermore, questions about any direct or indirect links within our architecture can be used to build quiz pages that are aimed at teaching the student the underlying concepts and their interrelations. For example, general questions like "what examinations can be performed on the specified anatomies" can be derived from the linkage of phenomena with examinations and anatomic structures.
Figure 3: MeSH anatomy structure as a tree-control element. The author can employ links for building information pages that teach the student the according associations of medical knowledge. Former experiences with tutoring software (Scheuerer et al. 1998) showed that relations we have made explicit in our architecture are an important subject matter in medical teaching. Information pages using text, pictures, animations or sounds illustrate both medical concepts and links between those concepts. Using concepts or concept combinations as indexes into databases of multimedia objects supports the author in creating those pages.
Figure 4: Sample quiz page derived from differential diagnoses and the final diagnosis for a phenomenon.
Supporting the Tutoring Process The links mentioned above not only support the authoring processes but also the automatic generation of navigation aids, online information windows, and feedback in the tutoring process. Menus based on hierarchies of examination procedures can be used to provide students in the tutoring process with possible choices for information gathering actions. The author is able to decide if the entire hierarchy or sub-hierarchies are shown to the student. It is also possible to focus examination methods on anatomic structures by using acquired links between them. In that case, a menu primarily showing an anatomic structure can be constructed. Every sub-menu has an additional menu item that pops up the according examination methods. This enables the student to select only those examinations that can be applied at specified anatomies. For example, links from the anatomy ‘thyroid’ to examinations like ‘thyroid function test’, ‘radioisotope scanning’, and ‘sonography’ could have been established in the case authoring process. Accordingly, this meaningful collection can be shown to the student as a selection of examination methods at the ‘thyroid’. Knowledge formalized within our architecture can be used for an automatic feedback generation in the tutoring process. We allow the quantitative or qualitative assessment of links between phenomenon-anatomy combinations and diagnoses, as it is done for example in the D3 expert system shell (Reinhardt 1997) by a number of positive and negative scores. By using this knowledge, comments and corrections that refer to a diagnosis chosen by the student can be automatically produced. To do this, the system searches for phenomena given in a tutoring case that plead for or rule out the diagnoses the student has chosen, according to the positive and negative links mentioned above. The student may be helped by this function at every stage of the tutoring process, because the described feedback methods take into account, what the student already knows about the patient. This approach can also be applied at links between diagnoses and therapies. Additionally, if not sufficient information about the patient was gathered by examination methods to confirm or exclude a diagnosis, the links mentioned above can be used to give hints to the necessary methods, or just to state that relevant information is still missing.
Conclusion The presented architecture allows an integration of different concept representation systems for the building of tutoring cases. Additionally, we establish links between these concepts to formalize systematic knowledge within medical cases. This supports the use of a common terminology for cooperating authors and helps to avoid redundant case information. Furthermore, authoring and tutoring processes are supported by mechanisms for generating quiz and information pages and by automatic feedback based on the acquired systematic knowledge. The presented work constitutes a first step in the project “Docs’n’ Drugs – Die virtuelle Poliklinik”. In this project, a tutoring system will be developed that shall represent a virtual clinic. Multiple clinical institutions, the University of Ulm, the University of Applied Science Ulm, and industrial partners are involved in this enterprise.
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