Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians. © 2006Bekhuis; licensee BioMed Central Ltd

A body-mind map:epidemiological and clinical aspects of the relation between somatic, depressive and anxiety symptomatology

Many persons with a depression or anxiety disorder also experience somatic symptoms like back ache or dizziness. Although it is well-known that these symptoms mutually influence each other and can respond to the same therapies, only little is known about the roles of individual symptoms in this relation. This is the result of a tendency to focus on groups of symptoms in research: feeling down is in this way considered to be interchangeable with anxiety and insomnia. However, these symptoms have highly differential characteristics. Having more insight into these characteristics could help to understand how people develop specific symptoms and how they can be treated most effectively. Ella Bekhuis investigated in her thesis the role of individual symptoms of depression, anxiety and the body. By using a novel method (‘network analysis’), a map was constructed that showed how dozens of symptoms were connected to each other. Each symptoms had a unique role in relation to other symptoms on this map. Anxiety, for instance, strongly increased the risk that someone felt down, while insomnia increased this risk only slightly. This map indicated the importance of taking into account such unique characteristics in research and practice to understand the relation between these symptoms. It was also examined how the map of symptoms was affected by treatment with antidepressants and/or psychotherapy. Each symptoms responded in a different way to these therapies. Anxiety showed for example a greater improvement after treatment with antidepressants than with psychotherapy, while no difference was found between the therapies for insomnia. Feeling down, on the other hand, seemed to respond better to antidepressants, but only because it showed a simultaneous improvement with other symptoms like anxiety. Based on the symptoms patients experienced before treatment, it could be predicted which therapy would be most effective for them. Taking into account the unique roles of individual symptoms can therefore help to personalize treatment in psychiatry

EDDA Study Designs Taxonomy (version 2.0)

The EDDA Study Designs Taxonomy (v2.0) was developed by the Evidence in Documents, Discovery, and Analytics (EDDA) Group: Tanja Bekhuis (Principal Scientist); Eugene Tseytlin (Systems Developer); Ashleigh Faith (Taxonomist); Faina Linkov (Epidemiologist). This work was made possible, in part, by the US National Library of Medicine, National Institutes of Health, grant no. R00LM010943. Foundational research is described in Bekhuis T, Demner Fushman D, Crowley RS. Comparative effectiveness research designs: an analysis of terms and coverage in Medical Subject Headings (MeSH) and Emtree. Journal of the Medical Library Association (JMLA). 2013 April;101(2):92-100. PMC3634392. Coverage of the terminology appearing in JMLA was extended with terms from MeSH, NCI Thesaurus (NCI), Emtree, the HTA Database Canadian Repository [international repository for health technology assessment], and Robert Sandieson's synonym ring for research synthesis. Collected terms were enriched with terms from the NCI Metathesaurus. Variants include synonyms for preferred terms, singular and plural forms, and American and British spellings. Definitions, if they exist, are mainly from MeSH, NCI, Emtree, and medical dictionaries. The EDDA Study Designs Taxonomy by Tanja Bekhuis and Eugene Tseytlin is licensed under a Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International License

Організація взаємодії слідчих та оперативних служб ОВС при розкритті й розслідуванні злочинів, пов’язаних із викраденням людей

Досліджується проблема організації взаємодії слідчих та оперативних служб при розкритті та розслідуванні злочинів, пов’язаних з викраденням людей.Исследуется проблема организации взаимодействия следователей и оперативных служб при раскрытии и расследовании преступлений, связанных с похищением людей.The problem of the organization of interoperability of inspectors and operative services at disclosing and investigation of the crimes connected with kidnapping

Towards automatic data extraction from clinical research reports: A case study of a systematic review of oral pain relief

In healthcare, it takes a long time for new treatments to move from clinical studies into practice: perhaps an average of 17 years [Balas et al., 2000]. Systematic review is a critical step in this research translation process because it determines what is known. To do this, a systematic review analyzes all available evidence on a particular question through a series of steps, including data extraction. The current best practice for data extraction is for two people to independently identify and extract data from each research paper. Because the data extraction step is almost always performed manually, it is very time-consuming [Tsafnat et al., 2014] yet methodological errors may cause problems with the review's conclusions [Lundh et al., 2009]. Our long-term goal is to help reviewers synthesize the literature quickly and accurately by developing a semi-automatic support system for data extraction. Towards this end, we are currently conducting an in-depth case study of a single systematic review, a Cochrane Review about oral pain relief. Through manual annotation and a content analysis of the six studies synthesized by this Cochrane Review, we will develop hypotheses about which clinical data elements can be automatically extracted. We will also develop an annotated corpus which will enable us to propose methods for automatically supporting human reviewers in data extraction. Eventually, we plan to design a semi-automated support system, and to test the two hypotheses (1) that it can reduce the time and human labor required to conduct a review and (2) that it can maintain or increase the quality of the resulting review.Ope

Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies

Background:: Statistical validity and model complexity are both important concepts to enhanced understanding and correctness assessment of computational models. However, information about these are often missing from publications applying machine learning. Aim: The aim of this study is to show the importance of providing details that can indicate statistical validity and complexity of models in publications. This is explored in the context of citation screening automation using machine learning techniques. Method: We built 15 Support Vector Machine (SVM) models, each developed using word2vec (average word) features --- and data for 15 review topics from the Drug Evaluation Review Program (DERP) of the Agency for Healthcare Research and Quality (AHRQ). Results: The word2vec features were found to be sufficiently linearly separable by the SVM and consequently we used the linear kernels. In 11 of the 15 models, the negative (majority) class used over 80% of its training data as support vectors (SVs) and approximately 45% of the positive training data. Conclusions: In this context, exploring the SVs revealed that the models are overly complex against ideal expectations of not more than 2%-5% (and preferably much less) of the training vectors

Terugkeer van de otter in het rivierengebied

In het bestek van dit onderzoek is gekeken naar de kansen voor een duurzame populatie otters in het rivierengebied. Uit deze analyse komen de Gelderse Poort, de uiterwaarden van de IJssel en de Waal naar voren met elk een potentiële otterpopulatie van ca. 30 dieren. Ook in Noorden Midden-Limburg (Maasdal en haar zijbeken) vormt gezamenlijk een potentieel gebied voor ca. 50 otters. De belangrijkste knelpunten voor de ontwikkeling van een duurzame otterpopulatie liggen op het vlak van infrastructuurdichtheid. Otters lopen als oeverbewonend zoogdier een groot risico om in het verkeer te sneuvelen. De actuele waterkwaliteit van de grote rivieren vormt geen grote beperking meer voor de terugkeer van de otter

Differential associations of specific depressive and anxiety disorders with somatic symptoms

AbstractObjectivePrevious studies have shown that depressive and anxiety disorders are strongly related to somatic symptoms, but much is unclear about the specificity of this association. This study examines the associations of specific depressive and anxiety disorders with somatic symptoms, and whether these associations are independent of comorbid depressive and anxiety disorders.MethodsCross-sectional data were derived from The Netherlands Study of Depression and Anxiety (NESDA). A total of 2008 persons (mean age: 41.6years, 64.9% women) were included, consisting of 1367 patients with a past-month DSM-diagnosis (established with the Composite International Diagnostic Interview [CIDI]) of depressive disorder (major depressive disorder, dysthymic disorder) and/or anxiety disorder (generalized anxiety disorder, social phobia, panic disorder, agoraphobia), and 641 controls. Somatic symptoms were assessed with the somatization scale of the Four-Dimensional Symptom Questionnaire (4DSQ), and included cardiopulmonary, musculoskeletal, gastrointestinal, and general symptoms. Analyses were adjusted for covariates such as chronic somatic diseases, sociodemographics, and lifestyle factors.ResultsAll clusters of somatic symptoms were more prevalent in patients with depressive and/or anxiety disorders than in controls (all p<.001). Multivariable logistic regression analyses showed that all types of depressive and anxiety disorders were independently related to somatic symptoms, except for dysthymic disorder. Major depressive disorder showed the strongest associations. Associations remained similar after adjustment for covariates.ConclusionThis study demonstrated that depressive and anxiety disorders show strong and partly differential associations with somatic symptoms. Future research should investigate whether an adequate consideration and treatment of somatic symptoms in depressed and/or anxious patients improve treatment outcomes