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Biomedical subjects

Y Shahar

Publications and source records attributed to Y Shahar.

At least 19 recordsLinked to original sources
PubMed1999

Representation of change in controlled medical terminologies.

Computer-based systems that support health care require large controlled terminologies to manage names and meanings of data elements. These terminologies are not static, because change in health care is inevitable. To share data and applications in health care, we need standards not only for terminologies and concept representation, but also for representing change. To develop a principled approach to managing change, we analyze the requirements of controlled medical terminologies and consider features that frame knowledge-representation systems have to offer. Based on our analysis, we present a concept model, a set of change operations, and a change-documentation model that may be appropriate for controlled terminologies in health care. We are currently implementing our modeling approach within a computational architecture.

Artificial Intelligence↗
PubMed1998

Timing is everything. Time-oriented clinical information systems.

Time is important in clinical information systems. Representing, maintaining, querying, and reasoning about time-oriented clinical data is a major theoretical and practical research area in medical informatics. In this nonexhaustive overview, we present a brief synopsis of research efforts in designing and developing time-oriented information systems in medicine. These efforts can be viewed from either an application point of view, distinguishing between different clinical tasks (such as diagnosis versus therapy) and clinical areas (such as infectious diseases versus oncology), or a methodological point of view, distinguishing between different theoretical approaches. We also explore the two primary methodological and theoretical paths research has taken in the past decade: temporal reasoning and temporal data maintenance. Both of these research areas include efforts to model time, temporal entities, and temporal queries. Collaboration between the two areas is possible, through tasks such as the abstraction of raw time-oriented clinical data into higher-level meaningful clinical concepts and the management of different levels of temporal granularity. Such collaboration could provide a common ground and useful areas for future research and development. We conclude with our view of future research directions.

Artificial Intelligence↗
PubMed1998

Knowledge-based visualization of time-oriented clinical data.

We describe a domain-independent framework (KNAVE) specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration in a context-sensitive manner through time-oriented raw clinical data and the multiple levels of higher-level, interval-based concepts that can be abstracted from these data. The KNAVE exploration operators, which are independent of any particular clinical domain, access a knowledge base of temporal properties of measured data and interventions that is specific to the clinical domain. Thus, domain-specific knowledge underlies the domain-independent semantics of the interpretation, visualization, and exploration processes. Initial evaluation of the KNAVE prototype by a small number of users with variable clinical and informatics training has been encouraging.

Artificial Intelligence↗
PubMed1998

Intention-based critiquing of guideline-oriented medical care.

We present a methodology and tool for providing retrospective review and critiquing of guideline-based medical care given to patients. We show how our guideline representation language, Asbru, which supports the use of physicians intentions in addition to physician's actions, allows us to compare the care given to a patient at the level of the intention to treat in addition to the more detailed plan carried out. We have developed an algorithm based on this representation for retrospective quality assessment of guideline-based care. Our method takes the physician's and institution's preferences and policies into account in explaining or justifying physician deviations from the recommendations of a guideline.

Algorithms↗
PubMed1997

Temporal reasoning and temporal data maintenance in medicine: issues and challenges.

We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medicine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.

Artificial Intelligence↗
PubMed1997

A temporal database mediator for protocol-based decision support.

To meet the data-processing requirements for protocol-based decision support, a clinical data-management system must be capable of creating high-level summaries of time-oriented patient data, and of retrieving those summaries in a temporally meaningful fashion. We previously described a temporal-abstraction module (RESUME) and a temporal-querying module (Chronus) that can be used together to perform these tasks. These modules had to be coordinated by individual applications, however, to resolve the temporal queries of protocol planners. In this paper, we present a new module that integrates the previous two modules and that provides for their coordination automatically. The new module can be used as a standalone system for retrieving both primitive and abstracted time-oriented data, or can be embedded in a larger computational framework for protocol-based reasoning.

Artificial Intelligence↗
PubMed1997

Development of a change model for a controlled medical vocabulary.

Managing change in controlled medical vocabularies is labor intensive and costly, but change is inevitable if vocabularies are to be kept up to date. The changes that are appropriate for a controlled medical vocabulary depend on the data stored for that vocabulary, and those data in turn depend on the needs of users. The set of change operations is the change model; the data stored about concepts comprise the concept model. Because the change model depends directly on the concept model, a discussion of the former necessitates a discussion of the latter. In this paper, we first present a set of tasks that we believe controlled medical vocabularies should handle. Next, we describe our concept model for a controlled medical vocabulary. Then, we review the literature on changes in existing vocabulary systems. Finally, we present our change model. We call our system, which incorporates the concept model and change model, the General Online Dictionary of Medicine (GOLDMINE).

Models, Theoretical↗
PubMed1996

Knowledge-based temporal abstraction in clinical domains.

We have defined a knowledge-based framework for the creation of abstract, interval-based concepts from time-stamped clinical data, the knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes its task into five subtasks; for each subtask we propose a formal solving mechanism. Our framework emphasizes explicit representation of knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse and sharing. The RESUME system implements the KBTA method. We tested RESUME in several clinical-monitoring domains, including the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert diabetes-therapy temporal-abstraction knowledge. Two diabetes-therapy experts (including the first one) created temporal abstractions from about 800 points of diabetic-patients' data. RESUME generated about 80% of the abstractions agreed by both experts; about 97% of the generated abstractions were valid. We discuss the advantages and limitations of the current architecture.

Artificial Intelligence↗
PubMed1996

Knowledge acquisition for temporal abstraction.

Temporal abstraction is the task of detecting relevant patterns in data over time. The knowledge-based temporal-abstraction method uses knowledge about a clinical domain's contexts, external events, and parameters to create meaningful interval-based abstractions from raw time-stamped clinical data. In this paper, we describe the acquisition and maintenance of domain-specific temporal-abstraction knowledge. Using the PROTEGE-II framework, we have designed a graphical tool for acquiring temporal knowledge directly from expert physicians, maintaining the knowledge in a sharable form, and converting the knowledge into a suitable format for use by an appropriate problem-solving method. In initial tests, the tool offered significant gains in our ability to rapidly acquire temporal knowledge and to use that knowledge to perform automated temporal reasoning.

Artificial Intelligence↗
PubMed1996

An intention-based language for representing clinical guidelines.

Automated support for guideline-based care would be enhanced considerably by a standard representation of clinical guidelines. To faciliate use and reuse, we suggest a representation that includes the explicit intentions of the guideline's author. These intentions include the desirable actions of the care provider and the patient states to be achieved before, during, and after the administration of the guideline. Intentions are temporal patterns of provider actions or patient states to be maintained, achieved, or avoided. We view automated support as a collaborative effort of the health-care provider and an automated assistant and involves several different tasks. We defined the syntax and, the semantics of a text-based language (ASBRU) for representation and annotation of clinical guidelines. The language supports maintenance of the automated assistant's knowledge base and could improve the quality and flexibility of the automated assistant's recommendations. In the ASGAARD project, we are developing reasoning mechanisms that use the ASBRU language for execution and critiquing tasks in conjunction with online electronic patient medical records.

Expert Systems↗
PubMed1995

Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support.

PROTEGE-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 PROTEGE-II can be applied to the task of providing protocol-based decision support in the domain of treating HIV-infected patients. To apply PROTEGE-II, (1) we construct a decomposable problem-solving method called episodic skeletal-plan refinement, (2) we build an application ontology that consists of the terms and relations in the domain, and of method-specific distinctions not already captured in the domain terms, and (3) we specify mapping relations that link terms from the application ontology to the domain-independent terms used in the problem-solving method. From the application ontology, we automatically generate a domain-specific knowledge-acquisition tool that is custom-tailored for the application. The knowledge-acquisition tool is used for the creation and maintenance of domain knowledge used by the problem-solving method. The general goal of the PROTEGE-II approach is to produce systems and components that are reusable and easily maintained. This is the rationale for constructing ontologies and problem-solving methods that can be composed from a set of smaller-grained methods and mechanisms. This is also why we tightly couple the knowledge-acquisition tools to the application ontology that specifies the domain terms used in the problem-solving systems. 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.

Artificial Intelligence↗
PubMed1995

Knowledge-based temporal abstraction in diabetes therapy.

We suggest a general framework for solving the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this problem-solving framework as the knowledge-based temporal-abstraction (KBTA) method. The KBTA method emphasizes explicit representation, acquisition, maintenance, reuse, and the sharing of knowledge required for abstraction of time-oriented clinical data. We describe the subtasks into which the KBTA method decomposes its task, the problem-solving mechanisms that solve these subtasks, and the knowledge necessary for instantiating these mechanisms in a particular clinical domain. We have implemented the KBTA method in the RESUME system and have applied it to the task of monitoring the care of insulin-dependent diabetics.

Artificial Intelligence↗
PubMed1994

A temporal-abstraction mediator for protocol-based decision-support systems.

The inability of many clinical decision-support applications to integrate with existing databases limits the wide-scale deployment of such systems. To overcome this obstacle, we have designed a data-interpretation module that can be embedded in a general architecture for protocol-based reasoning and that can support the fundamental task of detecting temporal abstractions. We have developed this software module by coupling two existing systems--RESUME and Chronus--that provide complementary temporal-abstraction techniques at the application and the database levels, respectively. Their encapsulation into a single module thus can resolve the temporal queries of protocol planners with the domain-specific knowledge needed for the temporal-abstraction task and with primary time-stamped data stored in autonomous clinical databases. We show that other computer methods for the detection of temporal abstractions do not scale up to the data- and knowledge-intensive environments of protocol-based decision-support systems.

Artificial Intelligence↗
PubMed1994

Knowledge-based temporal abstraction for diabetic monitoring.

We have developed a general method that solves the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this method as knowledge-based temporal-abstraction (KBTA). In this paper, we focus on the knowledge representation, acquisition, maintenance, reuse and sharing aspects of the KBTA method. We describe five problem-solving mechanisms that solve the five subtasks into which the KBTA method decomposes its task, and four types of knowledge necessary for instantiating these mechanisms in a particular domain. We present an example of instantiating the KBTA method in the clinical area of monitoring insulin-dependent-diabetes patients.

Artificial Intelligence↗
PubMed1993

RESUME: a temporal-abstraction system for patient monitoring.

RESUME is a system that performs temporal abstraction of time-stamped data. The temporal-abstraction task is crucial for planning treatment, for executing treatment plans, for identifying clinical problems, and for revising treatment plans. The RESUME system is based on a model of three basic temporal-abstraction mechanisms: point temporal abstraction, a mechanism for abstracting the values of several parameters into a value of another parameter; temporal inference, a mechanism for inferring sound logical conclusions over a single interval or two meeting intervals; and temporal interpolation, a mechanism for bridging nonmeeting temporal intervals. Making explicit the knowledge required for temporal abstraction supports the acquisition and the sharing of that knowledge. We have implemented the RESUME system using the CLIPS knowledge-representation shell. The RESUME system emphasizes the need for explicit representation of temporal-abstraction knowledge, and the advantages of modular, task-specific but domain-independent architectures for building medical knowledge-based systems.

Abstracting and Indexing↗
PubMed1993

Knowledge reuse: temporal-abstraction mechanisms for the assessment of children's growth.

Currently, many workers in the field of medical informatics realize the importance of knowledge reuse. The PROTEGE-II project seeks to develop and implement a domain-independent framework that allows system builders to create custom-tailored role-limiting methods from generic reusable components. These new role-limiting methods are used to create domain- and task-specific knowledge-acquisition tools with which an application expert can generate domain- and task-specific decision-support systems. One required set of reusable components embodies the problem-solving knowledge to generate temporal abstractions. Previously, members of the PROTEGE-II project have used these temporal-abstraction mechanisms to infer the presence of myelotoxicity in patients with AIDS. In this paper, we show that these mechanisms are reusable in the domain of assessment of children's growth.

Artificial Intelligence↗
PubMed1992

A temporal-abstraction system for patient monitoring.

RESUME is a system that performs temporal abstraction of time-stamped data. RESUME is based on a model of three temporal-abstraction mechanisms: point temporal abstraction (a mechanism for abstracting values of several parameters into a value of another parameter); temporal inference (a mechanism for inferring sound logical conclusions over a single interval or two meeting intervals); and temporal interpolation (a mechanism for bridging nonmeeting temporal intervals). Making explicit the knowledge required for temporal abstraction supports the acquisition of that knowledge.

Artificial Intelligence↗