Clinical guidelines can be viewed as generic skeletal-plan schemata that represent clinical procedural knowledge and that are instantiated and refined dynamically by care providers over significant time periods. In the Asgaard project, we are investigating a set of tasks that support the application of clinical guidelines by a care provider other than the guideline's designer. We are focusing on application of the guideline, recognition of care providers' intentions from their actions, and critique of care providers' actions given the guideline and the patient's medical record. We are developing methods that perform these tasks in multiple clinical domains, given an instance of a properly represented clinical guideline and an electronic medical patient record. In this paper, we point out the precise domain-specific knowledge required by each method, such as the explicit intentions of the guideline designer (represented as temporal patterns to be achieved or avoided). We present a machine-readable language, called Asbru, to represent and to annotate guidelines based on the task-specific ontology. We also introduce an automated tool for acquisition of clinical guidelines based on the same ontology, developed using the PROTEGE-II framework.

Skeletal plans are a powerful way to reuse existing domain-specific procedural knowledge. They are

PROTG-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTG-II can be applied to the task of providing protocol-based decision support in the domain of treating HIVinfected patients. For this task, we use a problem-solving method called episodic skeletal-plan refinement. This method is decomposable; we construct it from a set of reusable components. In addition, we build an application ontology that consists of the terms and relations in the domain, plus terms that supply method-specific knowledge requirements. From this ontology, we automatically generate a domain-specific knowledge-acquisition tool. The general goal of the PROTG-II approach is to produce systems and components that are easily maintained and reusable. This is the rationale for constructing a problem-solving method from a set of smaller-grained methods and mechanisms. This is also why our knowledge-acquisition tools are domain-specific and generated automatically from ontologies. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system. Keywords. Decision support; expert systems; knowledge acquisition.

this paper, we adopt Newell's perspective [51] of a "knowledge level"' analysis rather than addressing this topic at the "symbol level". Practical plan management requires a "knowledge rich" model [69] that facilitates efficient reasoning given the demands of the surrounding environment. According to our medical interest, approaches dealing with time handling, context, and incomplete information about the world's states and the effects of actions (dynamically changing environments) are most important.

Metalevel tools can support the software development process by automating the design of task- and application-specific tools. Dash is a metalevel tool that allows developers to generate domain-specific knowledge-acquisition tools from domain ontologies. Domain specialists use the knowledge-acquisition tools generated by dash to instantiate the concepts and relationships defined in the domain ontologies. The output of the knowledge-acquisition tools is a collection of instances that constitute the knowledge base for a knowledge-based system.

We consider the problem of providing automated support for guideline-based clinical care. Clinical guidelines are a common format in medical domains for prescribing a set of rules and policies that an attending physician should follow. In terms of an AI planning task, clinical guidelines can be viewed as a shared library of highly reusable skeletal reactive plans, whose details need to be refined by the executing planner over significant periods of time. The application of clinical guidelines involves the collection and interpretation of patient-related data, the application of prespecified plans, and a revision of the plans when necessary. Over the past decade, several research groups have implemented reactive-planning architectures specific for the task of refining skeletal plans over time. The importance of such systems is increasing as more clinical data are being captured and represented in an electronic format, and as quality control of medical care grows in importance. Conductin...

Skeletal plans are a powerful way to reuse existing domain-specific procedural knowledge. The main drawbacks are that the compositions and the interdependencies of different skeletal plans and their components are not lucid. The aim of this paper is to overcome these limitations and to present the visualization of time-oriented, skeletal plans. Within the Asgaard project, we have developed a time-oriented and intention-based language, called Asbru, to represent such skeletal plans. The Asbru syntax is defined in Backus-Naur form (BNF). Reading BNF or similar forms are next to impossible even for domain experts. We explored different representations and automated knowledge-acquisition tools. However, the domain experts did not accept any of these representations. Consequently, we investigated different metaphor graphics and ended up with a plan visualization utilizing the metaphors of "tracks" and "traffic", called AsbruView. We formatively evaluated different approaches ...

Time-Oriented Queries: The Tzolkin System John H. Nguyen, Yuval Shahar, Samson W. Tu, Amar K. Das, and Mark A. Musen Stanford Medical Informatics, Stanford University School of Medicine Stanford, CA 94305-5479 John H. Nguyen (corresponding author) Stanford Medical Informatics Medical School Office Building X-215 251 Campus Drive Stanford University Stanford, CA 94305-5479 Voice: +1-650-725-3399 Fax: +1-650-725-7944 Email: jnguyen@smi.stanford.edu Yuval Shahar Email: shahar@smi.stanford.edu Samson W. Tu Email: tu@smi.stanford.edu Amar K. Das Email: das@smi.stanford.edu Mark A. Musen Email: musen@smi.stanford.edu Abstract The ability to reason with time-oriented data is central to the practice of medicine. Monitoring clinical variables over time often provides information that drives medical decision making (e.g., clinical diagnosis and therapy planning). Because the time-oriented patient data are often stored in electronic databases, it is important to ensure that clinicians and medical decision-support applications can conveniently find answers to their clinical queries using these databases. To help clinicians and decision-support applications make medical decisions using time-oriented data, a database-management system should (1) permit the expression of abstract, time-oriented queries, (2) permit the retrieval of data that satisfy a given set of time-oriented data-selection criteria, and (3) present the retrieved data at the appropriate level of abstraction. We impose these criteria to facilitate the expression of clinical queries and to reduce the manual data processing that users must undertake to decipher the answers to their queries. We describe a system, Tzolkin, that integrates a general method for temporal-data maintenance with a gener...

this paper, when we combine a domain ontology with an enumeration of the instances intended by that ontology for a particular application, the resulting set of classes and instances is what we call a domain model.

Clinical guidelines reuse existing clinical procedural knowledge while leaving room for flexibility by the care provider applying that knowledge. Guidelines can be viewed as generic skeletal-plan schemata that are instantiated and refined dynamically by the care provider over significant periods of time and in highly dynamic environments. In the Asgaard project, we are investigating a set of tasks that support the application of clinical guidelines by a care provider other than the guideline's designer. We are focusing on application of the guideline, recognition of care providers' intentions from their actions, and critique of care providers' actions given the guideline and the patient's medical record. We are developing methods that perform these tasks in multiple clinical domains, given an instance of a properly represented clinical guideline and an electronic medical patient record. In this paper, we point out the precise domainspecific knowledge required by each method, such as the explicit intentions of the guideline designer (represented as temporal patterns to be achieved or avoided). We present a machine-readable language, called Asbru, to represent and to annotate guidelines based on the task-specific ontology. We also introduce an automated tool for acquisition of clinical guidelines based on the same ontology; the tool was developed using the PROTG-II framework's suite of tools.