PubMed · 11453143
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N M Bass. 2001-06-23. Tooth notations.. https://pubmed.ncbi.nlm.nih.gov/11453143/
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The discovery of the double helix half a century ago has so far been slow to affect medical practice, but significant transformations are likely over the next 50 years. Changes to the way medicine is practised and new doctors are trained will be required before potential benefits are realized.
OBJECTIVES: We evaluated the usefulness of two models for integrating nursing diagnosis concepts into SNOMED Clinical Terms (CT). METHODS: First, we dissected nursing diagnosis term phrases from two source terminologies (North American Nursing Diagnosis Association Taxonomy 1 (NANDA) and Omaha System) into the semantic categories of the European Committee for Standardization (CEN) categorical structure and ISO reference terminology model (RTM). Second, we critically analyzed the similarities between the semantic links in the CEN and ISO models and the semantic links used to formally define diagnostic concepts in SNOMED CT. RESULTS: Our findings demonstrated that focus, bearer/subject of information, and judgment were present in 100% of the NANDA and Omaha term phrases. The Omaha term phrases contained no additional descriptors beyond those considered mandatory in the CEN and ISO models. The comparison among the semantic links showed that SNOMED CT currently contains all but one of the semantic links needed to model the two source terminologies for integration. In conclusion, our findings support the potential utility of the CEN and ISO models for integrating nursing diagnostic concepts into SNOMED CT.
Reproducing kernel Hilbert space (RKHS) methods provide a unified context for solving a wide variety of statistical modelling and function estimation problems. We consider two such problems: We are given a training set [yi, ti, i = 1, em leader, n], where yi is the response for the ith subject, and ti is a vector of attributes for this subject. The value of y(i) is a label that indicates which category it came from. For the first problem, we wish to build a model from the training set that assigns to each t in an attribute domain of interest an estimate of the probability pj(t) that a (future) subject with attribute vector t is in category j. The second problem is in some sense less ambitious; it is to build a model that assigns to each t a label, which classifies a future subject with that t into one of the categories or possibly "none of the above." The approach to the first of these two problems discussed here is a special case of what is known as penalized likelihood estimation. The approach to the second problem is known as the support vector machine. We also note some alternate but closely related approaches to the second problem. These approaches are all obtained as solutions to optimization problems in RKHS. Many other problems, in particular the solution of ill-posed inverse problems, can be obtained as solutions to optimization problems in RKHS and are mentioned in passing. We caution the reader that although a large literature exists in all of these topics, in this inaugural article we are selectively highlighting work of the author, former students, and other collaborators.