Biomedical subjects

Robert Moskovitch

Publications and source records attributed to Robert Moskovitch.

6 recordsLinked to original sources

PubMed2007

A comparative evaluation of full-text, concept-based, and context-sensitive search.

OBJECTIVES: Study comparatively (1) concept-based search, using documents pre-indexed by a conceptual hierarchy; (2) context-sensitive search, using structured, labeled documents; and (3) traditional full-text search. Hypotheses were: (1) more contexts lead to better retrieval accuracy; and (2) adding concept-based search to the other searches would improve upon their baseline performances. DESIGN: Use our Vaidurya architecture, for search and retrieval evaluation, of structured documents classified by a conceptual hierarchy, on a clinical guidelines test collection. MEASUREMENTS: Precision computed at different levels of recall to assess the contribution of the retrieval methods. Comparisons of precisions done with recall set at 0.5, using t-tests. RESULTS: Performance increased monotonically with the number of query context elements. Adding context-sensitive elements, mean improvement was 11.1% at recall 0.5. With three contexts, mean query precision was 42% +/- 17% (95% confidence interval [CI], 31% to 53%); with two contexts, 32% +/- 13% (95% CI, 27% to 38%); and one context, 20% +/- 9% (95% CI, 15% to 24%). Adding context-based queries to full-text queries monotonically improved precision beyond the 0.4 level of recall. Mean improvement was 4.5% at recall 0.5. Adding concept-based search to full-text search improved precision to 19.4% at recall 0.5. CONCLUSIONS: The study demonstrated usefulness of concept-based and context-sensitive queries for enhancing the precision of retrieval from a digital library of semi-structured clinical guideline documents. Concept-based searches outperformed free-text queries, especially when baseline precision was low. In general, the more ontological elements used in the query, the greater the resulting precision.

Abstracting and Indexing↗

PubMed2006

Multiple hierarchical classification of free-text clinical guidelines.

OBJECTIVE: Manual classification of free-text documents within a predefined hierarchy, commonly required in the medical domain, is highly time consuming task. We present an approach based on supervised learning to automate the classification of clinical guidelines into predefined hierarchical conceptual categories. METHODS AND MATERIAL: Given a set of hierarchically categorized documents in the training stage the learning algorithm exploits the hierarchical structure of the concepts in order to overcome the low number of training examples. The classification task is thus decomposed into a continuous decision process, unlike searching within a decision tree, which follows the concept hierarchy and makes a single decision at each node on the path, multiple paths can be chosen. Classification is based on applying a similarity function at each concept. Several evaluation measures were used, based on the intended use of the hierarchy. In addition, conservative and aggressive stop-criterion strategies for stopping the search through the concept hierarchy were formulated. An evaluation of the approach, including several training methods and multiple evaluation measures, has been performed using a training set of 1136 guidelines from the National Guideline Clearing House set. RESULTS: Based on a test collection consisting of 1038 clinical practice guidelines (CPGs) classified along two hierarchies, of roughly 5000 concepts, in which each CPG was classified by a mean of 10 concepts, a variable precision was observed from 44% to 60% depending on the settings of the training methods. CONCLUSION: These results demonstrate the feasibility of the approach, especially when considering the low ratio of guidelines to classification indices (concepts) in the evaluation data set used here.

Algorithms↗

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

Vaidurya--a concept-based, context-sensitive search engine for 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 guide-lines 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. Preliminary evaluation of Vaidurya in a standard information task and a large guide-line repository is encouraging; formal evaluation is under way.

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↗