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Biomedical subjects

Stephen B Johnson

Publications and source records attributed to Stephen B Johnson.

5 recordsLinked to original sources

Central challenges facing the national clinical research enterprise.

Medical scientists and public health policy makers are increasingly concerned that the scientific discoveries of the past generation are failing to be translated efficiently into tangible human benefit. This concern has generated several initiatives, including the Clinical Research Roundtable at the Institute of Medicine, which first convened in June 2000. Representatives from a diverse group of stakeholders in the nation's clinical research enterprise have collaborated to address the issues it faces. The context of clinical research is increasingly encumbered by high costs, slow results, lack of funding, regulatory burdens, fragmented infrastructure, incompatible databases, and a shortage of qualified investigators and willing participants. These factors have contributed to 2 major obstacles, or translational blocks: impeding the translation of basic science discoveries into clinical studies and of clinical studies into medical practice and health decision making in systems of care. Considering data from across the entire health care system, it has become clear that these 2 translational blocks can be removed only by the collaborative efforts of multiple system stakeholders. The goal of this article is to articulate the 4 central challenges facing clinical research at present--public participation, information systems, workforce training, and funding; to make recommendations about how they might be addressed by particular stakeholders; and to invite a broader, participatory dialogue with a view to improving the overall performance of the US clinical research enterprise.

Biomedical Research↗

Representing nested semantic information in a linear string of text using XML.

XML has been widely adopted as an important data interchange language. The structure of XML enables sharing of data elements with variable degrees of nesting as long as the elements are grouped in a strict tree-like fashion. This requirement potentially restricts the usefulness of XML for marking up written text, which often includes features that do not properly nest within other features. We encountered this problem while marking up medical text with structured semantic information from a Natural Language Processor. Traditional approaches to this problem separate the structured information from the actual text mark up. This paper introduces an alternative solution, which tightly integrates the semantic structure with the text. The resulting XML markup preserves the linearity of the medical texts and can therefore be easily expanded with additional types of information.

Programming Languages↗

The sublanguage of cross-coverage.

At Columbia-Presbyterian Medical Center, free-text "Signout" notes are typed into the electronic record by clinicians for the purpose of cross-coverage. We plan to "unlock" information about adverse events contained in these notes in a subsequent project using Natural Language Processing (NLP). To better understand the requirements for parsing, Signout notes were compared to other common medical notes (ambulatory clinic notes and discharge summaries) on a series of quantitative metrics. They are shorter (mean length 59.25 words vs. 144.11 and 340.85 for ambulatory and discharge notes respectively) and use more abbreviations (26.88% vs. 20.07% and 3.57%). Despite being terser, Signout notes use less ambiguous abbreviations (8.34% vs. 9.09% and 18.02%). Differences were found using Relative Entropy and Squared Chi-square Distance in a novel fashion to compare these medical corpora. Signout notes appear to constitute a unique sublanguage of medicine. The implications for parsing free-text cross-coverage notes into coded medical data are discussed.

Linguistics↗

The cognitive demands of an innovative query user interface.

Too often, online searches for health information are time consuming and produce results that are not sufficiently precise to answer clinicians' or patients' questions. The PERSIVAL project is designed to circumvent this problem by personalizing and tailoring searches and presentation to the demands of the user and the particular clinical context. This paper focuses on a cognitive evaluation of one component of this project, a Query User Interface (QUI). The study examines the system's ability to allow users to easily and intuitively express their information needs. We performed several analyses including a cognitive walkthrough of the interface and quantitative estimations of cognitive load. The paper also presents a preliminary analysis of usability testing. The analyses suggest that there are features in the QUI that contribute to a greater cognitive load and result in greater effort on the part of the subject. The results of usability testing are consistent with these findings. However, subjects found it to be relatively easy and intuitive to generate well-formed queries using the interface. This study contributed to the iterative design of the interface and to the next generation of the PERSIVAL system.

Attitude to Computers↗

Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the UMLS.

UNLABELLED: Motivation. The UMLS has been used in natural language processing applications such as information retrieval and information extraction systems. The mapping of free-text to UMLS concepts is important for these applications. To improve the mapping, we need a method to disambiguate terms that possess multiple UMLS concepts. In the general English domain, machine-learning techniques have been applied to sense-tagged corpora, in which senses (or concepts) of ambiguous terms have been annotated (mostly manually). Sense disambiguation classifiers are then derived to determine senses (or concepts) of those ambiguous terms automatically. However, manual annotation of a corpus is an expensive task. We propose an automatic method that constructs sense-tagged corpora for ambiguous terms in the UMLS using MEDLINE abstracts. METHODS: For a term W that represents multiple UMLS concepts, a collection of MEDLINE abstracts that contain W is extracted. For each abstract in the collection, occurrences of concepts that have relations with W as defined in the UMLS are automatically identified. A sense-tagged corpus, in which senses of W are annotated, is then derived based on those identified concepts. The method was evaluated on a set of 35 frequently occurring ambiguous biomedical abbreviations using a gold standard set that was automatically derived. The quality of the derived sense-tagged corpus was measured using precision and recall. RESULTS: The derived sense-tagged corpus had an overall precision of 92.9% and an overall recall of 47.4%. After removing rare senses and ignoring abbreviations with closely related senses, the overall precision was 96.8% and the overall recall was 50.6%. CONCLUSIONS: UMLS conceptual relations and MEDLINE abstracts can be used to automatically acquire knowledge needed for resolving ambiguity when mapping free-text to UMLS concepts.

Abbreviations as Topic↗