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Maya Galperin

Publications and source records attributed to Maya Galperin.

7 recordsLinked to original sources

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↗

Evaluation of antivenom therapy in Vipera palaestinae bites.

BACKGROUND: Vipera palaestinae antivenom has been successfully used to treat systemic and progressive local manifestations inflicted by this snake. The clinical course of several envenomations created the impression that the recommended fixed dose regimen of antivenom (50 ml) may not always be sufficient. OBJECTIVE: To evaluate the V. palaestinae antivenom fixed dose regimen and to assess the need for repeated antivenom administration as well as possible adverse effects. METHODS: Retrospective review of prospectively collected poison center data over a one-year period. RESULTS: One hundred and twelve patients were evaluated, 48% of whom were treated with antivenom. Antivenom treatment resulted in complete disappearance of systemic manifestations with no relapse. Three patients required additional doses of antivenom for marked progressive local signs--one initially received 50 ml of antivenom and two others only 30 ml. Anaphylaxis and serum sickness were each observed in 3.7% of the treated patients. CONCLUSION: The fixed dose regimen of 50 ml V. palaestinae antivenom is efficacious for the treatment of systemic and progressive local manifestations caused by this snake. There is insufficient data on whether smaller doses can be successfully used for systemic manifestations and whether initial larger doses are justified for marked progressive local signs (e.g. involvement of an entire limb). Randomized prospective controlled studies are needed to elucidate these issues.

Animals↗

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↗

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↗

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↗

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↗

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↗