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K Spackman

Publications and source records attributed to K Spackman.

5 recordsLinked to original sources
PubMed2005

Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.

BACKGROUND: Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction. RESULTS: Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall), with high efficiency in the use of seed pairs. CONCLUSION: The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.

Algorithms↗
PubMed2001

The SNOMED RT Procedure Model.

BACKGROUND: SNOMED RT represents a fundamental change from prior versions of SNOMED. The logic-based structure of SNOMED RT enables concepts to be defined more explicitly, providing an opportunity to address inconsistencies and ambiguities present in prior SNOMED concept definitions. OBJECTIVES: Create a unifying organizational strategy for all SNOMED RT procedures, and remove ambiguities in procedure concept definitions. METHODS: A comprehensive model of procedures was developed, based on a set of guiding principles and a review of known existing models. RESULTS: All SNOMED RT procedures are categorized by a common set of "root procedures" (high level atomic actions), and are more explicitly defined by a shared set of defining relationships. CONCLUSIONS: While the objectives have largely been met, open issues continue to be addressed. The similarity between procedure models of SNOMED RT and the U.K. s Clinical Terms Version 3 is proving to greatly facilitate the full integration of the two terminologies into a merged vocabulary to be known as SNOMED Clinical Terms (SNOMED CT), slated to be released in the near future.

Clinical Medicine↗
PubMed1995

Applying the trauma triage rule to blunt trauma patients.

OBJECTIVE: To determine the accuracy of the Baxt Trauma Triage Rule (TTR: systolic blood pressure < 85 mm Hg; Glasgow Coma Scale-motor score < 5; or penetrating trauma to head, neck, or trunk) for prediction of major trauma in an independent data set of blunt trauma patients. METHODS: Retrospective evaluation of the TTR in a cohort of patients identified by Oregon Trauma System entry criteria. Accuracy for prediction of "major trauma" victims was measured using resource-based definitions of major trauma. Participants included 626 adult, blunt trauma patients at a level-I trauma center serving a metropolitan center of more than one million people. RESULTS: Of 524 patients with sufficient registry data to apply the TTR, 95 (18%) and 63 (12%) patients met the criteria for major trauma suggested by Baxt et al. and Emerman et al., respectively. Using the Baxt definition of major trauma, the TTR had a sensitivity of 74% (95% CI: 0.65-0.83) and a specificity of 84% (95% CI: 0.81-0.88). There were 25 significant false-negative results, including 12 patients requiring urgent laparotomy and four patients requiring emergency airway procedures. Using the Emerman definition of major trauma, sensitivity improved modestly to 76% (95% CI: 0.65-0.87) and specificity decreased slightly to 80% (95% CI: 0.77-0.84). CONCLUSIONS: In this blunt trauma population, the Baxt TTR failed to identify a significant number of severely injured patients. Slight alterations in the definition of "major trauma" can significantly affect the performance characteristics of triage instruments.

Adolescent↗
PubMed1993

A clinical laboratory information systems survey. A challenge for the decade.

In 1990, the College of American Pathologists Informatics Committee surveyed 14,785 laboratorians for their experiences with a clinical laboratory information system. A 16.25% response rate was achieved, representing 2402 questionnaires that were analyzed. Despite the perceived satisfaction of the clinical laboratory information system users with more expensive systems, no economy of scale was demonstrated with increasing system cost through either laboratory staff reduction or increased number of specimens per day. The strongest predictors of system satisfaction were (1) vendor success measured by number of installations and (2) a selection process that involved the pathologist/laboratory director and included a formal request for proposal. The need for integration of clinical laboratory information systems with hospital information systems, as well as the universal adoption of standard productivity terminology, including work load units, was evident.

Clinical Laboratory Information Systems↗