CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results: We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions: CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

PubDNA Finder in a Nutshell. Searching the Life Sciences Literature with Sequences of Nucleic Acids

Biomedical researchers and clinicians working with molecular technologies in routine clinical practice often need to review the available literature to gather information regarding specific sequences of nucleic acids. This includes, for instance, finding articles related to a concrete DNA sequence, or identifying empirically-validated primer/probe sequences to evaluate the presence of different micro-organisms. Unfortunately, these hard and time-consuming tasks often need to be manually performed by researchers themselves since no publicly available biomedical literature search engine, e.g. PubMed, PubMed Central (PMC), etc., provides the required search functionalities. In this article, we describe PubDNA Finder, a web service that enables users to perform advanced searches on PubMed Central-indexed full text articles with sequences of nucleic acids

Win-win transport strategies: searching for sinergies

In order to achieve to minimize car-based trips, transport planners have been particularly interested in understanding the factors that explain modal choices. In the transport modelling literature there has been an increasing awareness that socioeconomic attributes and quantitative variables are not sufficient to characterize travelers and forecast their travel behavior. Recent studies have also recognized that users? social interactions and land use patterns influence travel behavior, especially when changes to transport systems are introduced, but links between international and Spanish perspectives are rarely deal. In this paper, factorial and path analyses through a Multiple-Indicator Multiple-Cause (MIMIC) model are used to understand and describe the relationship between the different psychological and environmental constructs with social influence and socioeconomic variables. The MIMIC model generates Latent Variables (LVs) to be incorporated sequentially into Discrete Choice Models (DCM) where the levels of service and cost attributes of travel modes are also included directly to measure the effect of the transport policies that have been introduced in Madrid during the last three years in the context of the economic crisis. The data used for this paper are collected from a two panel smartphone-based survey (n=255 and 190 respondents, respectively) of Madrid.

Searching for a consensus similarity function for generalized trapezoidal fuzzy numbers

There is controversy regarding the use of the similarity functions proposed in the literature to compare generalized trapezoidal fuzzy numbers since conflicting similarity values are sometimes output for the same pair of fuzzy numbers. In this paper we propose a similarity function aimed at establishing a consensus. It accounts for the different approaches of all the similarity functions. It also has better properties and can easily incorporate new parameters for future improvements. The analysis is carried out on the basis of a large and representative set of pairs of trapezoidal fuzzy numbers.

Win-Win transport strategies: searching for synergies.

The need of an urban transport strategy on urban areas which solves the environmental problems derived from traffic without decreasing the trip attraction of these urban areas is taken for granted. Besides there is also a clear consensus among researchers and institutions in the need for integrated transport strategies (May et al., 2006; Zhang et al., 2006). But there is still a lack of knowledge on the policy measures to be implemented. This research aims to deepen in the understanding of how do different measures interact when implemented together: synergies and complementarities between them. The methodological approach to achieve this objective has been the double analysis ? quantitative and comprehensive ? of the different impacts produced, first of all by each of the measures by itself, and later on combining these measures. For this analysis, we have first defined the objectives to achieve within the transport strategy ? emissions and noise decrease without losing trip attraction - , and then selecting the measures to test the effects these objectives generate. This selection has been based on a literature review, searching for measures with have proven to be successful in achieving at least one of the objectives. The different policies and policy combinations have been tested in a multimodal transport model, considering the city of Madrid as case study. The final aim of the research is to find a transport strategy which produces positive impact in all the objectives established, this is a win-win strategy.