Evaluation of the clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures.
(49/1313)
OBJECTIVE: The purpose of this study was to test the adequacy of the Clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures. METHODS: After extension of the definitions, 1, 096 items from 35 standardized assessment instruments were dissected into the elements of the Clinical LOINC semantic structure. An additional coder dissected at least one randomly selected item from each instrument. When multiple scale types occurred in a single instrument, a second coder dissected one randomly selected item representative of each scale type. RESULTS: The results support the adequacy of the Clinical LOINC semantic structure as a terminology model for standardized assessments. Using the revised definitions, the coders were able to dissect into the elements of Clinical LOINC all the standardized assessment items in the sample instruments. Percentage agreement for each element was as follows: component, 100 percent; property, 87.8 percent; timing, 82.9 percent; system/sample, 100 percent; scale, 92.6 percent; and method, 97.6 percent. DISCUSSION: This evaluation was an initial step toward the representation of standardized assessment items in a manner that facilitates data sharing and re-use. Further clarification of the definitions, especially those related to time and property, is required to improve inter-rater reliability and to harmonize the representations with similar items already in LOINC. (+info)
Embedded structures and representation of nursing knowledge.
(50/1313)
Nursing Vocabulary Summit participants were challenged to consider whether reference terminology and information models might be a way to move toward better capture of data in electronic medical records. A requirement of such reference models is fidelity to representations of domain knowledge. This article discusses embedded structures in three different approaches to organizing domain knowledge: scientific reasoning, expertise, and standardized nursing languages. The concept of pressure ulcer is presented as an example of the various ways lexical elements used in relation to a specific concept are organized across systems. Different approaches to structuring information-the clinical information system, minimum data sets, and standardized messaging formats-are similarly discussed. Recommendations include identification of the polyhierarchies and categorical structures required within a reference terminology, systematic evaluations of the extent to which structured information accurately and completely represents domain knowledge, and modifications or extensions to existing multidisciplinary efforts. (+info)
Automated mapping of observation codes using extensional definitions.
(51/1313)
OBJECTIVE: To create "extensional definitions" of laboratory codes from derived characteristics of coded values in a clinical database and then use these definitions in the automated mapping of codes between disparate facilities. DESIGN: Repository data for two laboratory facilities in the Intermountain Health Care system were analyzed to create extensional definitions for the local codes of each facility. These definitions were then matched using automated matching software to create mappings between the shared local codes. The results were compared with the mappings of the vocabulary developers. MEASUREMENTS: The number of correct matches and the size of the match group were recorded. A match was considered correct if the corresponding codes from each facility were included in the group. The group size was defined as the total number of codes in the match group (e.g., a one-to-one mapping is a group size of two). RESULTS: Of the matches generated by the automated matching software, 81 percent were correct. The average group size was 2.4. There were a total of 328 possible matches in the data set, and 75 percent of these were correctly identified. CONCLUSIONS: Extensional definitions for local codes created from repository data can be utilized to automatically map codes from disparate systems. This approach, if generalized to other systems, can reduce the effort required to map one system to another while increasing mapping consistency. (+info)
An evaluation of ICNP intervention axes as terminology model components.
(52/1313)
In this paper we evaluate selected axes of the International Classification of Nursing Practice (ICNP) as terminology model components for nursing actions by dissecting and categorizing two data sets of term phrases (Patient Care Data Set and Home Health Care Classification). Second, we critically analyze the relationships between the ICNP axes and terminology model components used to formally define procedures (including nursing actions) in SNOMED RT. Our findings demonstrate that the semantic categories represented by the ICNP intervention axes are relevant sources for terminology model components for nursing actions. In addition, our findings suggest that only minimal additions or extensions to the current semantic links of SNOMED RT are needed to support the formal definition of nursing actions such as those contained in PCDS and HHCC. (+info)
NLP techniques associated with the OpenGALEN ontology for semi-automatic textual extraction of medical knowledge: abstracting and mapping equivalent linguistic and logical constructs.
(53/1313)
This research project presents methodological and theoretical issues related to the inter-relationship between linguistic and conceptual semantics, analysing the results obtained by the application of a NLP parser to a set of radiology reports. Our objective is to define a technique for associating linguistic methods with domain specific ontologies for semi-automatic extraction of intermediate representation (IR) information formats and medical ontological knowledge from clinical texts. We have applied the Edinburgh LTG natural language parser to 2810 clinical narratives describing radiology procedures. In a second step, we have used medical expertise and ontology formalism for identification of semantic structures and abstraction of IR schemas related to the processed texts. These IR schemas are an association of linguistic and conceptual knowledge, based on their semantic contents. This methodology aims to contribute to the elaboration of models relating linguistic and logical constructs based on empirical data analysis. Advance in this field might lead to the development of computational techniques for automatic enrichment of medical ontologies from real clinical environments, using descriptive knowledge implicit in large text corpora sources. (+info)
Using semantic distance for the efficient coding of medical concepts.
(54/1313)
OBJECTIVE: To use the notion of semantic distance to find the nearest neighbors of a medical concept in a controlled vocabulary. MATERIAL AND METHOD: 392 concepts from the cardiovascular chapter of the ICD-10 were projected on the axes of SNOMED III. Distances were measured on each axis and the resulting distance was found using a Lp norm. RESULTS: The distance between a set of ischemic diseases and a set of non-ischemic diseases was significant (p < 0.0001). Our method was validated by finding the k nearest neighbors of ten different diagnoses from the ICD-10 cardiovascular chapter. DISCUSSION: The availability of SNOMED-RT should improve our method. Several more steps are necessary to provide an ideal coding tool. (+info)
A randomized controlled trial of concept based indexing of Web page content.
(55/1313)
OBJECTIVE: Medical information is increasingly being presented in a web-enabled format. Medical journals, guidelines, and textbooks are all accessible in a web-based format. It would be desirable to link these reference sources to the electronic medical record to provide education, to facilitate guideline implementation and usage and for decision support. In order for these rich information sources to be accessed via the medical record they will need to be indexed by a single comparable underlying reference terminology. METHODS: We took a random sample of 100 web pages out of the 6,000 web pages on the Mayo Clinic's Health Oasis web site. The web pages were divided into four datasets each containing 25 pages. These were humanly reviewed by four clinicians to identify all of the health concepts present (R1DA, R2DB, R3DC, R4DD). The web pages were simultaneously indexed using the SNOMED-RT beta release. The indexing engine has been previously described and validated. A new clinician reviewed the indexed web pages to determine the accuracy of the automated mappings as compared with the human identified concepts (R4DA, R3DB, R2DC, R1DD). RESULTS: This review found 13,220 health concepts. Of these 10,383 concepts were identified by the initial human review (78.5% +/- 3.6%). The automated process identified 10,083 concepts correctly (76.3% +/- 4.0%) from within this corpus. The computer identified 2,420 concepts, which were not identified by the clinician's review but were upon further consideration important to include as health concepts. There was on average a 17.1% +/- 3.5% variability in the human reviewers ability to identify the important health concepts within web page content. Concept Based Indexing provided a positive predictive value (PPV) of finding a health concept of 79.3% as compared with keyword indexing which only has a PPV of 33.7% (p < 0.001). CONCLUSION: SNOMED-RT is a reasonable ontology for web page indexing. Concept based indexing provides a significantly greater accuracy in identifying health concepts when compared with keyword indexing. (+info)
Automated coding of diagnoses--three methods compared.
(56/1313)
In Germany, new legal requirements have raised the importance of the accurate encoding of admission and discharge diseases for in- and outpatients. In response to emerging needs for computer-supported tools we examined three methods for automated coding of German-language free-text diagnosis phrases. We compared a language-independent lexicon-free n-gram approach with one which uses a dictionary of medical morphemes and refines the query by a mapping to SNOMED codes. Both techniques produced a ranked output of possible diagnoses within a vector space framework for retrieval. The results did not reveal any significant difference: The correct diagnosis was found in approximately 40% for three-digit codes, and 30% for four-digit codes. The lexicon-based method was then modified by substituting the vector space ranking by a heuristic approach that capitalizes on the semantic structure of SNOMED, thus raising the number of correct diagnoses significantly (approximately 50% for three-digit codes, and 40% for four-digit codes). As a result, we claim that lexicon-based retrieval methods do not perform better than the lexicon-free ones, unless conceptual knowledge is added. (+info)