Biomedical subjects

Yuval Shahar

Publications and source records attributed to Yuval Shahar.

11 recordsLinked to original sources

PubMed2005

A framework for distributed mediation of temporal-abstraction queries to clinical databases.

OBJECTIVE: The specification and creation of a distributed system that integrates medical knowledge bases with time-oriented clinical databases; the goal is to answer complex temporal queries regarding both raw data and its abstractions, such as are often required in medical applications. METHODS: (1) Specification, design, and implementation of a generalized access method to a set of heterogeneous clinical data sources, by using a virtual medical-record interface and by mapping the local terms to a set of standardized medical vocabularies; (2) specification of a generalized interface to a set of knowledge sources; (3) specification and implementation of a service, called ALMA that computes complex time-oriented medical queries that include both raw data and abstractions derivable from it; (4) design and implementation of a mediator, called IDAN, that answers raw-data and abstract queries by integrating the appropriate clinical data with the relevant medical knowledge and uses the computation service to answer the queries; (5) an expressive language that enables definition of time-dependent medical queries, which are referred to the mediator; (6) evaluation of the effect of the system, when combined with a new visual interface, called KNAVE-II, on the speed and accuracy of answering a set of complex queries in an oncology sub domain, by a group of clinicians, compared to answering these queries using paper or an electronic spreadsheet. RESULTS: We have implemented the full IDAN architecture. The IDAN/KNAVE-II combination significantly increased the accuracy and speed of answering complex queries about both the data and their abstractions, compared to the standard tools. CONCLUSION: The implemented architecture proves the feasibility of the distributed integration of medical knowledge sources with clinical data of heterogeneous sources. The results suggest that the proposed IDAN modular architecture has potential significance for supporting the automation of clinical tasks such as diagnosis, monitoring, therapy, and quality assessment.

Artificial Intelligence↗

PubMed2004

A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools.

Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, not in a formal, executable format, and are not easily accessible to clinicians at the point of care. We introduce 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 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. The DeGeL tools for support of guideline-based care operate, at some level, on all guideline ontologies. We have demonstrated the feasibility of the architecture and the tools for several guideline ontologies, including Asbru and GEM.

Computer Communication Networks↗

PubMed2004

A knowledge-based time-oriented active database approach for intelligent abstraction, querying and continuous monitoring of clinical data.

Query and interpretation of time-oriented medical data involves two subtasks: Temporal-reasoning--intelligent analysis of time-oriented data, and temporal-maintenance--effective storage, query, and retrieval of these data. Integration of these tasks into one system, known as temporal-mediator, has been proven to be beneficial to biomedical applications such as monitoring, therapy, quality assessment, visualization and exploration of time-oriented data. One potential problem in existing temporal-mediation approaches is lack of sufficient responsiveness when querying or continuously monitoring the database for complex abstract concepts that are derived from the raw data, especially regarding a large patient group. We propose a new approach: the knowledge-based time-oriented active database, a temporal extension of the active-database concept, and a merger of temporal reasoning and temporal maintenance within a persistent database framework. The approach preserves the efficiency of databases in handling data storage and retrieval, while enabling specification and performance of complex temporal reasoning using an incremental-computation approach. We implemented our approach within the Momentum system. Initial experiments are encouraging; an evaluation is underway

Algorithms↗

PubMed2004

Evaluation of KNAVE-II: a tool for intelligent query and exploration of patient data.

We present the results of a preliminary evaluation of KNAVE-II, a distributed knowledge-based computational framework for visualization, interpretation, and exploration of longitudinal clinical data and of multiple levels of concepts derivable from these data. KNAVE-II uses a distributed architecture to access at run-time clinical time-oriented data, a domain-specific knowledge base containing properties of the clinical data, and a knowledge-based problem-solving method for computing on-the-fly interpretations of these data. The purpose of the evaluation was to compare efficiency and user satisfaction when answering clinical queries of variable complexity about clinical time-oriented data using KNAVE-II, versus using methods available in standard clinical settings: paper chart or electronic spreadsheet (ESS). Subjects answered high-complexity queries significantly faster using KNAVE-II than when using paper or ESS. User satisfaction with KNAVE-II was significantly superior compared to satisfaction using paper or ESS, based on a standard usability scale. Users also explicitly ranked KNAVE-II as superior to paper and the ESS.

Artificial Intelligence↗

PubMed2004

An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols.

We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.

Algorithms↗

PubMed2004

A multiple-ontology customizable search interface for retrieval of clinical guidelines.

A major problem in the effective use of clinical guidelines is fast and accurate access at the point of care. Thus, we are developing a digital electronic guideline library (DeGeL) and a set of tools for incremental conversion of free-text guidelines into increasingly machine-comprehensible representations, which support automated application. Even if guidelines are represented in electronic fashion, care providers need to be able to quickly retrieve the guidelines that best fit the clinical situation at hand. We describe Vaidurya, a search and retrieval engine that exploits the hybrid nature of guideline representation in the DeGeL architecture. Vaidurya can use not only free-text keywords, but also multiple semantic indices along which the guidelines are classified, and the mark up of guidelines in DeGeL, using the semantic roles of one or more guideline-representation languages (ontologies). Vaidurya offers a wide variety of querying options, in order to enable different types of users to query the guideline library in a manner that is both efficient and user friendly. We describe the customizable query interface, in which each user can create their own personal query interface.

Expert Systems↗

PubMed2004

The Digital electronic Guideline Library (DeGeL): a hybrid framework for representation and use of clinical guidelines.

We propose to present a poster (and potentially also a demonstration of the implemented system) summarizing the current state of our work on a hybrid, multiple-format representation of clinical guidelines that facilitates conversion of guidelines from free text to a formal representation. We describe a distributed Web-based architecture (DeGeL) and a set of tools using the hybrid representation. The tools enable performing tasks such as guideline specification, semantic markup, search, retrieval, visualization, eligibility determination, runtime application and retrospective quality assessment. The representation includes four parallel formats: Free text (one or more original sources); semistructured text (labeled by the target guideline-ontology semantic labels); semiformal text (which includes some control specification); and a formal, machine-executable representation. The specification, indexing, search, retrieval, and browsing tools are essentially independent of the ontology chosen for guideline representation, but editing the semi-formal and formal formats requires ontology-specific tools, which we have developed in the case of the Asbru guideline-specification language. The four formats support increasingly sophisticated computational tasks. The hybrid guidelines are stored in a Web-based library. All tools, such as for runtime guideline application or retrospective quality assessment, are designed to operate on all representations. We demonstrate the hybrid framework by providing examples from the semantic markup and search tools.

Artificial Intelligence↗

PubMed2003

Developing quality indicators and auditing protocols from formal guideline models: knowledge representation and transformations.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.

Algorithms↗

PubMed2003

A distributed, collaborative, structuring model for a clinical-guideline digital-library.

The Digital Electronic Guideline Library (DeGeL) is a Web-based framework and a set of distributed tools that facilitate gradual conversion of clinical guidelines from free text, through semi-structured text, to a fully structured, executable representation. Thus, guidelines exist in a hybrid, multiple-format representation The three formats support increasingly sophisticated computational tasks. The tools perform semantic markup, classification, search, and browsing, and support computational modules that we are developing, for run-time application and retrospective quality assessment. We describe the DeGeL architecture and its collaborative-authoring authorization model, which is based on (1) multiple medical-specialty authoring groups, each including a group manager who controls group authorizations, and (2) a hierarchical authorization model based on the different functions involved in the hybrid guideline-specification process. We have implemented the core modules of the DeGeL architecture and demonstrated distributed markup and retrieval using the knowledge roles of two guidelines ontologies (Asbru and GEM). We are currently evaluating several of the DeGeL tools.

Computer Communication Networks↗

PubMed2003

Interactive visualization and exploration of time-oriented clinical data using a distributed temporal-abstraction architecture.

KNAVE-II is a system for visualization and exploration of large amounts of time-oriented clinical data and of multiple levels of clinically meaningful abstractions derivable from these data. KNAVE-II uses a distributed temporal-abstraction architecture that integrates a set of knowledge services, each interacting with a domain-specific knowledge source, a set of data-access services, each interacting with a clinical data source, and a computational service for deriving knowledge-based abstractions of the data.

Artificial Intelligence↗

PubMed2003

A Web-Based system for interactive visualization and exploration of time-oriented clinical data and their abstractions.

In this theater-style demonstration, the speakers will demonstrate KNAVE-II, a Web-based distributed system for interactive visualization and exploration of large amounts of time-oriented clinical data from multiple sources, and of clinically meaningful concepts (abstractions) derivable from these data. The KNAVE-II system and its complete underlying architecture provide a solution to the data overload problem.

Artificial Intelligence↗