Menagerie: A text-mining tool to support animal-human translation in neurodegeneration research.

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.

A Knowledge Intensive Approach to Mapping Clinical Narrative to LOINC.

Many natural language processing systems are being applied to clinical text, yet clinically useful results are obtained only by honing a system to a particular context. We suggest that concentration on the information needed for this processing is crucial and present a knowledge intensive methodology for mapping clinical text to LOINC. The system takes published case reports as input and maps vital signs and body measurements and reports of diagnostic procedures to fully specified LOINC codes. Three kinds of knowledge are exploited: textual, ontological, and pragmatic (including information about physiology and the clinical process). Evaluation on 4809 sentences yielded precision of 89% and recall of 93% (F-score 0.91). Our method could form the basis for a system to provide semi-automated help to human coders.

Knowledge-based methods to help clinicians find answers in MEDLINE.

OBJECTIVES: Large databases of published medical research can support clinical decision making by providing physicians with the best available evidence. The time required to obtain optimal results from these databases using traditional systems often makes accessing the databases impractical for clinicians. This article explores whether a hybrid approach of augmenting traditional information retrieval with knowledge-based methods facilitates finding practical clinical advice in the research literature. DESIGN: Three experimental systems were evaluated for their ability to find MEDLINE citations providing answers to clinical questions of different complexity. The systems (SemRep, Essie, and CQA-1.0), which rely on domain knowledge and semantic processing to varying extents, were evaluated separately and in combination. Fifteen therapy and prevention questions in three categories (general, intermediate, and specific questions) were searched. The first 10 citations retrieved by each system were randomized, anonymized, and evaluated on a three-point scale. The reasons for ratings were documented. MEASUREMENTS: Metrics evaluating the overall performance of a system (mean average precision, binary preference) and metrics evaluating the number of relevant documents in the first several presented to a physician were used. RESULTS: Scores (mean average precision = 0.57, binary preference = 0.71) for fusion of the retrieval results of the three systems are significantly (p < 0.01) better than those for any individual system. All three systems present three to four relevant citations in the first five for any question type. CONCLUSION: The improvements in finding relevant MEDLINE citations due to knowledge-based processing show promise in assisting physicians to answer questions in clinical practice.

Using UMLS metathesaurus concepts to describe medical images: dermatology vocabulary.

Web servers at the National Library of Medicine (NLM) displayed images of ten skin lesions to practicing dermatologists and provided an online form for capturing text they used to describe the pictures. The terms were submitted to the UMLS Metathesaurus (Meta). Concepts retrieved, their semantic types, definitions and synonyms, were returned to each subject in a second web-based form. Subjects rated the concepts against their own descriptive terms. They submitted 825 terms, 346 of which were unique and 300 mapped to UMLS concepts. The dermatologists rated 295 concepts as 'Exact Match' and they accomplished both tasks in about 30 min.

Cellular radio telecommunication for health care: benefits and risks.

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.