Academic literature on the topic 'Federated text retrieval'

RNPLS&T’s experience in providing open access and developing open archives is discussed. RNPLS&T’s heterogeneous information resources are evaluated; the structure of the inhouse digital resources in 2018 is described, including digitized books in RNPLS&T’s Electronic Archive, employees’ articles, articles in journals published by the Library, articles in the Library conferences proceedings, all hosted by the E-library, and the RNPLS&T’s information systems. The technologies of access to RNPLS&T’s digital resources are characterized, including in-house and commercial ones: access with Summon federated search system, access using Z39.50 and SRU/SRW protocols and access via the National Electronic Library access to the RNPLS&T’s resources, access to RNPLS&Ts E-library metadata using OAI-PMH protocol, etc. Integrated search external services are described. The technology of remote users access to individual subscriptions and licensed databases is specified. The current state of software solutions for server modules of the RNPLS&T’s open archive based on IRBIS64+ ILS is characterized.The goal is to provide users with the extended set of instruments for information retrieval and access to demanded full-text publications, including the external ones.

Background Staying up to date and answering clinical questions with current best evidence from health research is challenging. Evidence-based clinical texts, databases, and tools can help, but clinicians first need to translate their clinical questions into searchable queries. MacPLUS FS (McMaster Premium LiteratUre Service Federated Search) is an online search engine that allows clinicians to explore multiple resources simultaneously and retrieves one single output that includes the following: (1) evidence from summaries (eg, UpToDate and DynaMed), (2) preappraised research (eg, EvidenceAlerts), and (3) non-preappraised research (eg, PubMed), with and without validated bibliographic search filters. MacPLUS FS can also be used as a laboratory to explore clinical questions and evidence retrieval. Objective Our primary objective was to examine how clinicians formulate their queries on a federated search engine, according to the population, intervention, comparison, and outcome (PICO) framework. Our secondary objective was to assess which resources were accessed by clinicians to answer their questions. Methods We performed an analytical survey among 908 clinicians who used MacPLUS FS in the context of a randomized controlled trial on search retrieval. Recording account log-ins and usage, we extracted all 1085 queries performed during a 6-month period and classified each search term according to the PICO framework. We further categorized queries into background (eg, “What is porphyria?”) and foreground questions (eg, “Does treatment A work better than B?”). We then analyzed the type of resources that clinicians accessed. Results There were 695 structured queries, after exclusion of meaningless queries and iterations of similar searches. We classified 56.5% (393/695) of these queries as background questions and 43.5% (302/695) as foreground questions, the majority of which were related to questions about therapy (213/695, 30.6%), followed by diagnosis (48/695, 6.9%), etiology (24/695, 3.5%), and prognosis (17/695, 2.5%). This distribution did not significantly differ between postgraduate residents and medical faculty physicians (P=.51). Queries included a median of 3 search terms (IQR 2-4), most often related to the population and intervention or test, rarely related to the outcome, and never related to the comparator. About half of the resources accessed (314/610, 51.5%) were summaries, 24.4% (149/610) were preappraised research, and 24.1% were (147/610) non-preappraised research. Conclusions Our results, from a large sample of real-life queries, could guide the development of educational interventions to improve clinicians’ retrieval skills, as well as inform the design of more useful evidence-based resources for clinical practice. Trial Registration ClinicalTrials.gov NCT02038439; https://www.clinicaltrials.gov/ct2/show/NCT02038439

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018. Method: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers. Results: Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher. Conclusions: Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

Summary: The COVID19 pandemic created a surge in demand for critical care equipment against a backdrop of fast-moving geographic virus hotspots. A team from IBM Europe was put together to prove that a devolved healthcare system can be rapidly bridged by a mix of advanced and legacy technologies to provide a federated view of critical care equipment deployment and use during an emergency. This was achieved with the deployment of predictive analytics and blockchain, integrated with conventional hospital management system. The corollary investigation determined the manner in which this system can be harnessed in a postemergency recovery to provide a national supply chain efficiency backbone. Method: During a period of 2 weeks, a team of IBM consultants set up a technology sandbox environment to represent a network of an equipment manufacturer, a central national emergency monitoring center, and several hospitals managed by their respective trust organization. Within this environment, a hospital asset management system, Maximo, was configured to manage and track critical care equipment within a hospital; a blockchain traceability platform, IBM’s Blockchain Transparency System, was configured to ingest multiple hospital data reports; and a predictive analytic dashboard, Watson Analytics, would retrieve data from the blockchain platform to supplement other data sources to provide national views and support decision-making for the supply and movement of equipment. Three key principles in the design of this environment are speed, reuse, and minimal intrusion. Results: The hypothesis was to test whether the chosen technologies can overcome the challenges of misaligned demand and supply of critical care equipment during a national emergency. The execution of the tests led to successful simulation of three scenarios: (1) the tracking of the location and usage history of any single equipment that has been placed into the network; (2) the movement of equipment between independent hospitals is recorded and reported; (3) a real-time interrogation of the current location and status of all registered equipment. Conclusions: The successful completion of this proof of concept has demonstrated that emerging technology can be used to overcome poor macro level coordination and planning, which are the drawbacks of a devolved healthcare system. The corollary was that this proof also demonstrated that blockchain technology can be used to prolong the useful life of conventional technology.

Federated information retrieval is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot index uncrawlable hidden web collections; federated information retrieval systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated information retrieval systems acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. In this thesis, we propose new approaches for each of these problems. Our suggested methods, for collection representation, collection selection, and result merging, outperform state-of-the-art techniques in most cases. We also propose novel methods for estimating the number of documents in collections, and for pruning unnecessary information from collection representations sets. Although management of document duplication has been cited as one of the major problems in federated search, prior research in this area often assumes that collections are free of overlap. We investigate the effectiveness of federated search on overlapped collections, and propose new methods for maximizing the number of distinct relevant documents in the final merged results. In summary, this thesis introduces several new contributions to the field of federated information retrieval, including practical solutions to some historically unsolved problems in federated search, such as document duplication management. We test our techniques on multiple testbeds that simulate both hidden web and enterprise search environments.