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Discovery studies in animals constitute a cornerstone of biomedical research, but suffer from lack of generalizability to human populations. We propose that large-scale interrogation of these data could reveal patterns of animal use that could narrow the translational divide. We describe a text-mining approach that extracts translationally useful data from PubMed abstracts. These comprise six modules: species, model, genes, interventions/disease modifiers, overall outcome and functional outcome measures. Existing National Library of Medicine natural language processing tools (SemRep, GNormPlus and the Chemical annotator) underpin the program and are further augmented by various rules, term lists, and machine learning models. Evaluation of the program using a 98-abstract test set achieved F1 scores ranging from 0.75-0.95 across all modules, and exceeded F1 scores obtained from comparable baseline programs. Next, the program was applied to a larger 14,481 abstract data set (2008-2017). Expected and previously identified patterns of species and model use for the field were obtained. As previously noted, the majority of studies reported promising outcomes. Longitudinal patterns of intervention type or gene mentions were demonstrated, and patterns of animal model use characteristic of the Parkinson's disease field were confirmed. The primary function of the program is to overcome low external validity of animal model systems by aggregating evidence across a diversity of models that capture different aspects of a multifaceted cellular process. Some aspects of the tool are generalizable, whereas others are field-specific. In the initial version presented here, we demonstrate proof of concept within a single disease area, Parkinson's disease. However, the program can be expanded in modular fashion to support a wider range of neurodegenerative diseases.

Graphical abstractDisplay Omitted Integrating new domain knowledge into the UMLS extends SemRep core coverage.An existing ontology-building method is adapted to accommodate the new knowledge.Text analysis uncovers non-UMLS domain knowledge for a medical informatics case study.New concepts, semantic types, and relations are defined for the new domain.The new knowledge and semantic representation allow SemRep to apply to new domains. We describe a domain-independent methodology to extend SemRep coverage beyond the biomedical domain. SemRep, a natural language processing application originally designed for biomedical texts, uses the knowledge sources provided by the Unified Medical Language System (UMLS?). Ontological and terminological extensions to the system are needed in order to support other areas of knowledge. We extended SemRep's application by developing a semantic representation of a previously unsupported domain. This was achieved by adapting well-known ontology engineering phases and integrating them with the UMLS knowledge sources on which SemRep crucially depends. While the process to extend SemRep coverage has been successfully applied in earlier projects, this paper presents in detail the step-wise approach we followed and the mechanisms implemented. A case study in the field of medical informatics illustrates how the ontology engineering phases have been adapted for optimal integration with the UMLS. We provide qualitative and quantitative results, which indicate the validity and usefulness of our methodology.

We describe the use of a domain-independent methodology to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®) (Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information-savvy workforce. NLP and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user-friendly visualization application as a way to summarize results of PubMed searches in this field of knowledge.

A multipoint videoconference was webcast live to an audience who could commu- nicate with conference panelists and each other via chat. The videoconference, webcast, and chat were done entirely over the Internet. Seven panelists at four conference sites that had Internet2 con- nectivity and were located in different time zones within the continental United States discussed the topic of "Evaluating Health Professions Education and Information Resources on the Web." This discussion was broadcast to individuals and groups at various U.S. locations who had expressed an interest in the topic and had sufficient connectivity for receiving the video stream. Webcast recipi- ents could log on a chat server and type questions and comments to the panelists and other view- ers. The experiment's rationale, procedures, and outcomes are described, and issues associated with the use of the technologies are identified.

Objective: To compare three potential sources of controlled clinical terminology (READ codes version 3.1, SNOMED International, and Unified Medical Language System (UMLS) version 1.6) relative to attributes of completeness, clinical taxonomy, administrative mapping, term definitions and clarity (duplicate coding rate). Methods: The authors assembled 1929 source concept records from a variety of clinical information taken from four medical centers across the United States. The source data included medical as well as ample nursing terminology. The source records were coded in each scheme by an investigator and checked by the coding scheme owner. The codings were then scored by an independent panel of clinicians for acceptability. Codes were checked for definitions provided with the scheme. Codes for a random sample of source records were analyzed by an investigator for “parent” and “child” codes within the scheme. Parent and child pairs were scored by an independent panel of medical informatics specialists for clinical acceptability. Administrative and billing code mapping from the published scheme were reviewed for all coded records and analyzed by independent reviewers for accuracy. The investigator for each scheme exhaustively searched a sample of coded records for duplications. Results: SNOMED was judged to be significantly more complete in coding the source material than the other schemes (SNOMED* 70%; READ 57%; UMLS 50%; *p

Cellular radio telecommunication has increased exponentially with many applications to health care reported. The authors attempt to summarize published applications with demonstrated effect on health care, review briefly the rapid evolution of hardware and software standards, explain current limitations and future potential of data quality and security, and discuss issues of safety.

MEDLINE® retrieval using several information retrieval algorithms was characterized for relevance to point-of-care therapeutic decisions for a sample of clinical queries in family practice. Evaluation methodology is described and preliminary results are presented.

Dermatologists were shown digitized images of skin lesions compressed by Joint Photographic Expert Group (JPEG) and fractal image format (FIF) algorithms and asked to make a diagnostic assessment and rate the image quality in displaying the features necessary for that assessment. Dermatologists performance in the diagnostic assessment was not affected by the level of compression (up to 40×) using either algorithm, but ratings of image quality for all compressed images were significantly lower than for uncompressed images and for the original photographic slides. There were no significant differences in dermatologists' performance or ratings between the two compression algorithms.

A microcomputer-based system for copying, storing, retrieving, and displaying color still images was assembled from commercially available components. The system was tested against 35-mm color photographic transparencies by measuring the performance of 91 medical students in a task of identifying the primary morphology of eight skin lesion images from the displayed images. There was no significant difference in the students' performance between the two display media.

Automatic image annotation is an attractive approach for enabling convenient access to images found in a variety of documents. Since image captions and relevant discussions found in the text can be useful for summarizing the content of images, it is also possible that this text can be used to generate salient indexing terms. Unfortunately, this problem is generally domain-specific because indexing terms that are useful in one domain can be ineffective in others. Thus, we present a supervised machine learning approach to image annotation utilizing non-lexical features extracted from image-related text to select useful terms. We apply this approach to several subdomains of the biomedical sciences and show that we are able to reduce the number of ineffective indexing terms.