Biomedical subjects

Y Shahar

Publications and source records attributed to Y Shahar.

At least 19 recordsLinked to original sources

PubMed2006

Hybrid specification, storage, retrieval and runtime application of clinical guidelines.

Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, are not machine-comprehensible and are not easily accessible to clinicians at the point of care. We have designed and implemented a web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in the chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation and a formal representation. These formats support increasingly sophisticated computational tasks. Guidelines can thus be in a hybrid representation in which guidelines, and even parts of the same guideline, might exist at different formalisation levels. We have also developed and rigorously evaluated a methodology and an associated web-based tool, Uruz, for gradually structuring and semi-formalising free-text clinical guidelines. Finally, we have designed, implemented and evaluated a new approach, the hybrid runtime application model, for supporting runtime application of clinical guidelines that are not necessarily in a machine-comprehensible format; in particular, when the guideline is in a semi-formal representation and the patient's data are either in an electronic medical record or in a paper format. The tool implementing this new approach, the Spock module, is customised at this point to the Asbru guideline specification language and exploits the hybrid structure of guidelines in DeGeL. The Spock module also exploits our temporal-abstraction mediator to the patient record, IDAN, and our interactive intelligent-visualisation tool, KNAVE-II.

Decision Support Systems, Clinical↗

PubMed2002

Medical informatics: between science and engineering, between academia and industry.

OBJECTIVE: To analyze the nature and appropriate role of the Medical Informatics research and practice area in the 21st Century, and to determine its links to academic environments versus industrial companies and health-care organizations. METHODS: A qualitative analysis of the state of the art of Medical Informatics, based on observation of current medical informatics programs and research in academic and industrial sites. RESULTS AND CONCLUSIONS: Medical Informatics is definitely a scientific and technological area of endeavor, although somewhat ill-defined in scope. It is situated between science and engineering, but much closer to the engineering world, and its multidisciplinary nature fits well the engineering paradigm. It is better viewed as a specialization of the informatics field rather than as a basic medical science. However, there are good arguments as to why Medicine should be the first among equals to have its own informatics domain. Medical Informatics must have extensions to both academia and industry to survive. Medical informaticians, whether implicitly or explicitly, exist in three different environments: academic, clinical (user), and industrial (informatics developer); all three environments must be considered when trying to predict the future of this new multidisciplinary area.

Engineering↗

PubMed2001

Acquisition and analysis of repeating patterns in time-oriented clinical data.

OBJECTIVES: (1) Creation of an expressive language for specification of temporal patterns in clinical domains, (2) Development of a graphical knowledge-acquisition tool allowing expert physicians to define meaningful domain-specific patterns, (3) Implementation of an interpreter capable of detecting such patterns in clinical databases, and (4) Evaluation of the tools in the domains of diabetes and oncology. METHODS: We describe a constraint-based language, named CAPSUL, for specification of temporal patterns. We implemented a knowledge-acquisition tool and a temporal-pattern interpreter within Résumé, a larger temporal-abstraction architecture. We evaluated the knowledge-acquisition process with the help of domain experts. In collaboration with the Rush Presbyterian/St. Luke's Medical Center, we analyzed data of bone-marrow transplantation patients. The expert compared the detected patterns to a manual inspection of the data, with the help of an experimental information-visualization tool we are developing in a related project. RESULTS: The CAPSUL language was expressive enough during the knowledge-acquisition process to capture almost all of the patterns that the experts found useful. The patterns detected in the data by the pattern interpreter were all verified as correct. Completeness (whether all correct patterns were found) was difficult to assess, due to the size of the database. CONCLUSIONS: The CAPSUL language enables medical experts to express temporal patterns involving multiple levels of abstraction of clinical data. The ability to reuse both domain-patterns and abstract constraints seems highly useful. The Résumé interpreter, augmented by the CAPSUL semantics, finds the complex patterns within a clinical time-oriented database in a sound fashion.

Algorithms↗

PubMed2001

Medical quality assessment by scoring adherence to guideline intentions.

Quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe an approach for evaluating and consistently scoring clinician adherence to medical guidelines using the intentions of guideline authors. We present the Quality Indicator Language (QUIL) that may be used to formally specify quality constraints on physician behavior and patient outcomes derived from medical guidelines. We present a modeling and scoring methodology for consistently evaluating multi-step and multi-choice guideline plans based on guideline intentions and their revisions.

Algorithms↗

PubMed2000

Dimensions of time in illness: an objective view.

It is almost impossible to try to represent and analyze clinical data in the absence of a temporal dimension. The temporal aspect is especially important when automated decision support is used for patient care over substantial periods. This paper emphasizes the crucial role that tasks of temporal reasoning and temporal maintenance play in modern medical information and decision support systems; it also discusses the implications of providing automated support to clinicians who must perform such tasks as part of broader clinical tasks (for example, monitoring and therapy). Temporal reasoning tasks mainly involve intelligent analysis of time-oriented clinical data, and temporal maintenance tasks focus on effective storage and retrieval of these data. Both types of tasks, however, are highly relevant for such applications as patient monitoring, proper use of therapeutic guidelines, assessment of the quality of guideline use, and visualization and exploration of time-oriented biomedical data. Scientists in medical informatics should view the integration of these two areas as a major research and development goal. This paper demonstrates one approach to integration by presenting the concept of a temporal mediator, which combines temporal reasoning and temporal maintenance. Use of the temporal mediator in several clinical tasks is also presented and discussed.

Databases, Factual↗

PubMed2000

Change management of shared and local versions of health-care terminologies.

Local sites that adopt a shared health-care terminology for computer-based systems have local needs that prompt the local-terminology maintainers to make changes to the local version of the shared terminology. If the local site is motivated to conform to the shared terminology, then the burden lies with the local site to manage its own changes and to incorporate the changes of the shared version at periodic intervals. We call this process synchronization. We survey current approaches that address problems of sharing and local modification, and we present the CONCORDIA model, which supports carefully controlled divergence of a local version from a shared terminology. CONCORDIA provides the underlying design and methodology for the implementation of a synchronization-support tool.

Humans↗

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↗

PubMed1999

Intelligent visualization and exploration of time-oriented clinical data.

Physicians and other care providers often need to quickly browse and interpret large numbers of time-oriented clinical data. Reducing the information overload involving such tasks is a major goal for medical information systems. We describe a conceptual architecture and software implementation specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration of time-oriented clinical data and the multiple levels of meaningful concepts that can be derived from these data. We build on our work on abstraction of time-oriented clinical data using a knowledge base, acquired from expert physicians, of temporal properties of the data. The core module of the new framework is called KNAVE (Knowledge-based Navigation of Abstractions for Visualization and Explanation). Health care providers can manipulate the display though several visualization and exploration operators. These operators have semantics that are domain independent but that are customized automatically for the application by access to the domain-specific knowledge base. The display, which reflects data and derived interpretations in the patient's database, changes when the user explores key relations (e.g., the dependency hierarchy) in the knowledge base of the relevant clinical domain. Preliminary assessment of the initial prototype with several clinical users has been encouraging. The KNAVE methodology has broad ramifications for reducing the load that large numbers of time-oriented clinical data put on care providers.

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↗