Academic literature on the topic 'I2b2'

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Journal articles on the topic "I2b2"

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Wagholikar, Kavishwar B., Michael Mendis, Pralav Dessai, et al. "Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform." Biomedical Informatics Insights 10 (January 1, 2018): 117822261877774. http://dx.doi.org/10.1177/1178222618777749.

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Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at more than 150 institutions for querying patient data. An i2b2 installation (called hive) comprises several i2b2 cells that provide different functionalities. Given the complex architecture of i2b2 installation, creating a working installation of the platform is challenging for new users. This is despite the availability of extensive documentation for i2b2 and access to a large and active mailing list community of i2b2 users. To address this problem, we have created an automated installation package, called i2b2-quickstart, which automatically downloads the latest i2b2 source code and dependencies, and compiles and configures the i2b2 cells to create a functional i2b2 hive installation. This package will serve as a convenient starting point and reference implementation that will facilitate researchers in the installation and exploration of the i2b2 platform.
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Wagholikar, Kavishwar B., Joshua C. Mandel, Jeffery G. Klann, et al. "SMART-on-FHIR implemented over i2b2." Journal of the American Medical Informatics Association 24, no. 2 (2016): 398–402. http://dx.doi.org/10.1093/jamia/ocw079.

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We have developed an interface to serve patient data from Informatics for Integrating Biology and the Bedside (i2b2) repositories in the Fast Healthcare Interoperability Resources (FHIR) format, referred to as a SMART-on-FHIR cell. The cell serves FHIR resources on a per-patient basis, and supports the “substitutable” modular third-party applications (SMART) OAuth2 specification for authorization of client applications. It is implemented as an i2b2 server plug-in, consisting of 6 modules: authentication, REST, i2b2-to-FHIR converter, resource enrichment, query engine, and cache. The source code is freely available as open source. We tested the cell by accessing resources from a test i2b2 installation, demonstrating that a SMART app can be launched from the cell that accesses patient data stored in i2b2. We successfully retrieved demographics, medications, labs, and diagnoses for test patients. The SMART-on-FHIR cell will enable i2b2 sites to provide simplified but secure data access in FHIR format, and will spur innovation and interoperability. Further, it transforms i2b2 into an apps platform.
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Klann, Jeffrey G., Lori C. Phillips, Christopher Herrick, Matthew A. H. Joss, Kavishwar B. Wagholikar, and Shawn N. Murphy. "Web services for data warehouses: OMOP and PCORnet on i2b2." Journal of the American Medical Informatics Association 25, no. 10 (2018): 1331–38. http://dx.doi.org/10.1093/jamia/ocy093.

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Abstract Objective Healthcare organizations use research data models supported by projects and tools that interest them, which often means organizations must support the same data in multiple models. The healthcare research ecosystem would benefit if tools and projects could be adopted independently from the underlying data model. Here, we introduce the concept of a reusable application programming interface (API) for healthcare and show that the i2b2 API can be adapted to support diverse patient-centric data models. Materials and Methods We develop methodology for extending i2b2’s pre-existing API to query additional data models, using i2b2’s recent “multi-fact-table querying” feature. Our method involves developing data-model-specific i2b2 ontologies and mapping these to query non-standard table structure. Results We implement this methodology to query OMOP and PCORnet models, which we validate with the i2b2 query tool. We implement the entire PCORnet data model and a five-domain subset of the OMOP model. We also demonstrate that additional, ancillary data model columns can be modeled and queried as i2b2 “modifiers.” Discussion i2b2’s REST API can be used to query multiple healthcare data models, enabling shared tooling to have a choice of backend data stores. This enables separation between data model and software tooling for some of the more popular open analytic data models in healthcare. Conclusion This methodology immediately allows querying OMOP and PCORnet using the i2b2 API. It is released as an open-source set of Docker images, and also on the i2b2 community wiki.
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Wagholikar, Kavishwar B., Shreekanth V. Joshi, Vishal V. Pai Vernekar, et al. "Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform." BioMed Research International 2020 (July 8, 2020): 1–8. http://dx.doi.org/10.1155/2020/2851713.

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Despite the widespread use of the “Informatics for Integrating Biology and the Bedside” (i2b2) platform, there are substantial challenges for loading electronic health records (EHR) into i2b2 and for querying i2b2. We have previously presented a simplified framework for semantic abstraction of EHR records into i2b2. Building on our previous work, we have created a proof-of-concept implementation of cloud services on an i2b2 data store for cohort identification. Specifically, we have implemented a graphical user interface (GUI) that declares the key components for data import, transformation, and query of EHR data. The GUI integrates with Azure cloud services to create data pipelines for importing EHR data into i2b2, creation of derived facts, and querying for generating Sankey-like flow diagrams that characterize the patient cohorts. We have evaluated the implementation using the real-world MIMIC-III dataset. We discuss the key features of this implementation and direction for future work, which will advance the efforts of the research community for patient cohort identification.
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Klann, Jeffrey G., Aaron Abend, Vijay A. Raghavan, Kenneth D. Mandl, and Shawn N. Murphy. "Data interchange using i2b2." Journal of the American Medical Informatics Association 23, no. 5 (2016): 909–15. http://dx.doi.org/10.1093/jamia/ocv188.

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Abstract Objective Reinventing data extraction from electronic health records (EHRs) to meet new analytical needs is slow and expensive. However, each new data research network that wishes to support its own analytics tends to develop its own data model. Joining these different networks without new data extraction, transform, and load (ETL) processes can reduce the time and expense needed to participate. The Informatics for Integrating Biology and the Bedside (i2b2) project supports data network interoperability through an ontology-driven approach. We use i2b2 as a hub, to rapidly reconfigure data to meet new analytical requirements without new ETL programming. Materials and Methods Our 12-site National Patient-Centered Clinical Research Network (PCORnet) Clinical Data Research Network (CDRN) uses i2b2 to query data. We developed a process to generate a PCORnet Common Data Model (CDM) physical database directly from existing i2b2 systems, thereby supporting PCORnet analytic queries without new ETL programming. This involved: a formalized process for representing i2b2 information models (the specification of data types and formats); an information model that represents CDM Version 1.0; and a program that generates CDM tables, driven by this information model. This approach is generalizable to any logical information model. Results Eight PCORnet CDRN sites have implemented this approach and generated a CDM database without a new ETL process from the EHR. This enables federated querying within the CDRN and compatibility with the national PCORnet Distributed Research Network. Discussion We have established a way to adapt i2b2 to new information models without requiring changes to the underlying data. Eight Scalable Collaborative Infrastructure for a Learning Health System sites vetted this methodology, resulting in a network that, at present, supports research on 10 million patients’ data. Conclusion New analytical requirements can be quickly and cost-effectively supported by i2b2 without creating new data extraction processes from the EHR.
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Mate, S., K. Helbing, U. Sax, H. U. Prokosch, and T. Ganslandt. "Unlocking Data for Clinical Research – The German i2b2 Experience." Applied Clinical Informatics 02, no. 01 (2011): 116–17. http://dx.doi.org/10.4338/aci-2010-09-cr-0051.

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Summary Objective: Data from clinical care is increasingly being used for research purposes. The i2b2 platform has been introduced in some US research communities as a tool for data integration and querying by clinical users. The purpose of this project was to assess the applicability of i2b2 in Germany regarding use cases, functionality and integration with privacy enhancing tools. Methods: A set of four research usage scenarios was chosen, including the transformation and import of ontology and fact data from existing clinical data collections into i2b2 v1.4 instances. Query performance was measured in comparison to native SQL queries. A setup and administration tool for i2b2 was developed. An extraction tool for CDISC ODM data was programmed. Interfaces for the TMF privacy enhancing tools (PID Generator, Pseudonymization Service) were implemented. Results: Data could be imported in all tested scenarios from various source systems, including the generation of i2b2 ontology definitions. The integration of TMF privacy enhancing tools was possible without modification of the platform. Limitations were found regarding query performance in comparison to native SQL and certain temporal queries. Conclusions: i2b2 is a viable platform for data query tasks in use cases typical for networked medical research in Germany. The integration of privacy enhancing tools facilitates the use of i2b2 within established data protection concepts. Entry barriers should be lowered by providing tools for simplified setup and import of medical standard formats like CDISC ODM.
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Zapletal, Eric, Jean-Emmanuel Bibault, Philippe Giraud, and Anita Burgun. "Integrating Multimodal Radiation Therapy Data into i2b2." Applied Clinical Informatics 09, no. 02 (2018): 377–90. http://dx.doi.org/10.1055/s-0038-1651497.

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Background Clinical data warehouses are now widely used to foster clinical and translational research and the Informatics for Integrating Biology and the Bedside (i2b2) platform has become a de facto standard for storing clinical data in many projects. However, to design predictive models and assist in personalized treatment planning in cancer or radiation oncology, all available patient data need to be integrated into i2b2, including radiation therapy data that are currently not addressed in many existing i2b2 sites. Objective To use radiation therapy data in projects related to rectal cancer patients, we assessed the feasibility of integrating radiation oncology data into the i2b2 platform. Methods The Georges Pompidou European Hospital, a hospital from the Assistance Publique – Hôpitaux de Paris group, has developed an i2b2-based clinical data warehouse of various structured and unstructured clinical data for research since 2008. To store and reuse various radiation therapy data—dose details, activities scheduling, and dose-volume histogram (DVH) curves—in this repository, we first extracted raw data by using some reverse engineering techniques and a vendor's application programming interface. Then, we implemented a hybrid storage approach by combining the standard i2b2 “Entity-Attribute-Value” storage mechanism with a “JavaScript Object Notation (JSON) document-based” storage mechanism without modifying the i2b2 core tables. Validation was performed using (1) the Business Objects framework for replicating vendor's application screens showing dose details and activities scheduling data and (2) the R software for displaying the DVH curves. Results We developed a pipeline to integrate the radiation therapy data into the Georges Pompidou European Hospital i2b2 instance and evaluated it on a cohort of 262 patients. We were able to use the radiation therapy data on a preliminary use case by fetching the DVH curve data from the clinical data warehouse and displaying them in a R chart. Conclusion By adding radiation therapy data into the clinical data warehouse, we were able to analyze radiation therapy response in cancer patients and we have leveraged the i2b2 platform to store radiation therapy data, including detailed information such as the DVH to create new ontology-based modules that provides research investigators with a wider spectrum of clinical data.
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Klann, Jeffrey G., Michael Mendis, Lori C. Phillips, et al. "Taking advantage of continuity of care documents to populate a research repository." Journal of the American Medical Informatics Association 22, no. 2 (2014): 370–79. http://dx.doi.org/10.1136/amiajnl-2014-003040.

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Abstract Objective Clinical data warehouses have accelerated clinical research, but even with available open source tools, there is a high barrier to entry due to the complexity of normalizing and importing data. The Office of the National Coordinator for Health Information Technology's Meaningful Use Incentive Program now requires that electronic health record systems produce standardized consolidated clinical document architecture (C-CDA) documents. Here, we leverage this data source to create a low volume standards based import pipeline for the Informatics for Integrating Biology and the Bedside (i2b2) clinical research platform. We validate this approach by creating a small repository at Partners Healthcare automatically from C-CDA documents. Materials and methods We designed an i2b2 extension to import C-CDAs into i2b2. It is extensible to other sites with variances in C-CDA format without requiring custom code. We also designed new ontology structures for querying the imported data. Results We implemented our methodology at Partners Healthcare, where we developed an adapter to retrieve C-CDAs from Enterprise Services. Our current implementation supports demographics, encounters, problems, and medications. We imported approximately 17 000 clinical observations on 145 patients into i2b2 in about 24 min. We were able to perform i2b2 cohort finding queries and view patient information through SMART apps on the imported data. Discussion This low volume import approach can serve small practices with local access to C-CDAs and will allow patient registries to import patient supplied C-CDAs. These components will soon be available open source on the i2b2 wiki. Conclusions Our approach will lower barriers to entry in implementing i2b2 where informatics expertise or data access are limited.
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Maier, Christian, Jan Christoph, Danilo Schmidt, et al. "Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools." Journal of Healthcare Engineering 2019 (January 17, 2019): 1–10. http://dx.doi.org/10.1155/2019/5640685.

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The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2. Since i2b2 uses an entity-attribute-value (EAV) schema, which is a fundamentally different way of modeling data in comparison to a standard relational schema in TBase, we evaluated if (a) the data relationship of the source system entities can still be represented in the i2b2 schema and if (b) the IDRT is a suitable solution for loading the data of a comprehensive data schema like TBase into i2b2. For that reason, we identified entities in the TBase data schema which were relevant for answering questions on cohort identification. By doing so, we found out that the entities had different structures that needed to be handled differently for the ETL process. Furthermore, the use of IDRT revealed shortcomings with regard to large input data and specific data structures that are part of most modern EHR systems. However, this project also showed that our way of modeling the TBase data in i2b2 has been proven to be successful in terms of answering the most common questions of clinicians on cohort identification.
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Baum, B., J. Christoph, I. Engel, et al. "Integrated Data Repository Toolkit (IDRT)." Methods of Information in Medicine 55, no. 02 (2016): 125–35. http://dx.doi.org/10.3414/me15-01-0082.

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SummaryBackground: In recent years, research data warehouses moved increasingly into the focus of interest of medical research. Nevertheless, there are only a few center-independent infrastructure solutions available. They aim to provide a consolidated view on medical data from various sources such as clinical trials, electronic health records, epidemiological registries or longitudinal cohorts. The i2b2 framework is a well-established solution for such repositories, but it lacks support for importing and integrating clinical data and metadata.Objectives: The goal of this project was to develop a platform for easy integration and administration of data from heterogeneous sources, to provide capabilities for linking them to medical terminologies and to allow for transforming and mapping of data streams for user-specific views.Methods: A suite of three tools has been developed: the i2b2 Wizard for simplifying administration of i2b2, the IDRT Import and Mapping Tool for loading clinical data from various formats like CSV, SQL, CDISC ODM or biobanks and the IDRT i2b2 Web Client Plugin for advanced export options. The Import and Mapping Tool also includes an ontology editor for rearranging and mapping patient data and structures as well as annotating clinical data with medical terminologies, primarily those used in Germany (ICD-10-GM, OPS, ICD-O, etc.).Results: With the three tools functional, new i2b2-based research projects can be created, populated and customized to researcher’s needs in a few hours. Amalgamating data and metadata from different databases can be managed easily. With regards to data privacy a pseudonymization service can be plugged in. Using common ontologies and reference terminologies rather than project-specific ones leads to a consistent understanding of the data semantics.Conclusions: i2b2’s promise is to enable clinical researchers to devise and test new hypothesis even without a deep knowledge in statistical programing. The approach pre -sented here has been tested in a number of scenarios with millions of observations and tens of thousands of patients. Initially mostly observant, trained researchers were able to construct new analyses on their own. Early feedback indicates that timely and extensive access to their “own” data is appreciated most, but it is also lowering the barrier for other tasks, for instance checking data quality and completeness (missing data, wrong coding).
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Dissertations / Theses on the topic "I2b2"

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Sahlström, Jakob. "Automatic de-identification of case narratives from spontaneous reports in VigiBase." Thesis, Uppsala universitet, Avdelningen för datorteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-262158.

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The use of patient data is essential in research but it is on the other hand confidential and can only be used after acquiring approval from an Ethical Board and informed consent from the individual patient. A large amount of patient data is therefore difficult to obtain if sensitive information, such as names, id numbers and contact details, are not removed from the data, by so called de-identification. Uppsala Monitoring Centre maintains the world's larges database of individual case reports of any suspected adverse drug reaction. There exists, of today, no method for efficiently de-identifying the narrative text included in these which causes countries like the United States of America reports to exclude the narratives in the reports. The aim of this thesis is to develop and evaluate a method for automatic de-identification of case narratives in reports from the WHO Global Individual Case Safety Report Database System, VigiBase. This report compares three different models, namely Regular Expressions, used for text pattern matching, and the statistical models Support Vector Machine (SVM) and Conditional Random Fields (CRF). Performance, advantages and disadvantages are discussed as well as how identified sensitive information is handled to maintain readability of the narrative text. The models developed in this thesis are also compared to existing solutions to the de-identification problem. The 400 reports extracted from VigiBase were already well de-identified in terms of names, ID numbers and contact details, making it difficult to train statistical models on these categories. The reports did however, contain plenty of dates and ages. For these categories Regular Expression would be sufficient to achieve a good performance. To identify entities in other categories more advanced methods such as the SVM and CRF are needed and will require more data. This was prominent when applying the models on the more information rich i2b2 de-identification challenge benchmark data set where the statistical models developed in this thesis performed at a competing level with existing models in the literature.
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Griffier, Romain. "Intégration et utilisation secondaire des données de santé hospitalières hétérogènes : des usages locaux à l'analyse fédérée." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0479.

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Les données issues du soin peuvent être utilisées pour des finalités autres que celles pour lesquelles elles ont été collectées initialement : c’est l’utilisation secondaire des données de santé. Dans le contexte hospitalier, afin de lever les verrous de l’utilisation secondaire des données de santé (verrous liés aux données et verrous organisationnels), une stratégie classique consiste à mettre en place un Entrepôt de Données de Santé (EDS). Dans le cadre de cette thèse, trois contributions à l’EDS du CHU de Bordeaux sont décrites. Premièrement, une méthode d’alignement des data éléments de biologie numérique basée sur les instances et conforme aux règles de protection des données à caractère personnel est présentée, avec une F-mesure à 0,850, permettant de réduire l’hétérogénéité sémantique des données. Ensuite, une adaptation du modèle d’intégration des données cliniques d’i2b2 est proposée pour assurer la persistance des données d’un EDS dans une base de données NoSQL, Elasticsearch. Cette implémentation a été évaluée sur la base de données de l’EDS du CHU de Bordeaux et retrouve des performances améliorées en termes de stockage et de temps de requêtage, par rapport à une base de données relationnelle. Enfin, une présentation de l’environnement EDS du CHU de Bordeaux est réalisée, avec la description d’un premier EDS dédié aux usages locaux et qui peut être exploité en autonomie par les utilisateurs finaux (i2b2), et d’un second EDS, dédié aux réseaux fédérés (OMOP) permettant notamment la participation au réseau fédéré DARWIN-EU<br>Healthcare data can be used for purposes other than those for which it was initially collected: this is the secondary use of health data. In the hospital context, to overcome the obstacles to secondary use of healthcaree data (data and organizational barriers), a classic strategy is to set up Clinical Data Warehouses (CDWs). This thesis describes three contributions to the Bordeaux University Hospital’s CDW. Firstly, an instance-based, privacy-preserving, method for mapping numerical biology data elements is presented, with an F-measure of 0,850, making it possible to reduce the semantic heterogeneity of data. Next, an adaptation of the i2b2 clinical data integration model is proposed to enable CDW data persistence in a NoSQL database, Elasticsearch. This implementation has been evaluated on the Bordeaux University Hospital’s CDW, showing improved performance in terms of storage and query time, compared with a relational database. Finally, the Bordeaux University Hospital’s CDW environment is presented, with the description of a first CDW dedicated to local uses that can be used autonomously by end users (i2b2), and a second CDW dedicated to federated networks (OMOP) enabling participation in the DARWIN-EU federated network
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Lopes, Gonçalo José Pereira. "Financial literacy: financial knowledge of Portuguese individuals." Master's thesis, 2020. http://hdl.handle.net/10071/21490.

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Financial literacy is no longer a choice, but an obligation. People must be aware of the several financial issues that affect their daily lives, otherwise, they can be harmed by their decisions. The growing complexity of financial markets is originating more sophisticated financial products, therefore, individuals must possess the adequate knowledge to face these innumerous alternatives. In this context, this study aims to assess the financial literacy of Portuguese individuals, about some areas considered crucial for their financial well-being. It is important to understand the savings and investing habits of individuals, to identify their knowledge regarding credit and interest rates, as well as to identify their risk tolerance towards investing. A survey was conducted in 2020 (January - April), in order to obtain the necessary evidence for the study. The questions included several topics, thus enabling to collect data from different financial areas, such as savings, securities or interest rates. The sample comprises 240 Portuguese individuals, from 18 to 69 years old. The results show that the level of effective knowledge of participants is below 50%, which clearly represents an inadequate outcome. These results are in line with previous studies, thus meaning that even though there have been implemented specific programs focused on incrementing the financial knowledge of individuals, they still do not possess the necessary financial literacy that is required.<br>A literacia financeira já não é uma escolha, mas sim uma obrigação. As pessoas devem estar por dentro das diversas situações financeiras que afetam seu dia a dia, caso contrário, podem ser prejudicadas pelas suas próprias decisões. A crescente complexidade dos mercados financeiros está a dar origem a produtos financeiros cada vez mais sofisticados, portanto, os indivíduos devem possuir o conhecimento adequado para enfrentar essas inúmeras alternativas. Neste contexto, o presente estudo pretende avaliar a literacia financeira dos portugueses em algumas áreas consideradas cruciais para o seu bem-estar financeiro. É importante compreender os hábitos de poupança e investimento das pessoas, identificar os seus conhecimentos sobre crédito e taxas de juro, mas também perceber a sua tolerância ao risco nos seus investimentos. Em 2020 (janeiro-abril), foi realizada um questionário com o intuito de obter as evidências necessárias para o desenvolvimento deste estudo. As perguntas abrangeram diversos tópicos permitindo obter dados de várias áreas financeiras, como por exemplo a poupança, títulos ou taxas de juros. A amostra é composta por 240 portugueses, com idades compreendidas entre os 18 e os 69 anos. Os resultados mostram que o nível de conhecimento efetivo dos participantes é inferior a 50%, o que representa claramente um resultado inadequado. Esses resultados estão em linha com estudos anteriores, portanto, embora tenham sido implementados programas específicos com foco na melhoria do conhecimento financeiro dos indivíduos, estes ainda não possuem a literacia financeira que lhes é exigida.
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Books on the topic "I2b2"

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Chavez, Bonnie. i2B: Introduction2Business. Cognella Academic Publishing, 2018.

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Chavez, Bonnie. I2b: Introduction2Business. Cognella, Inc., 2018.

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I2E2: Leading Lasting Change. Creative Health Care Management, 2007.

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Felgen, Jayne. I2e2: Leading Lasting Change. Creative Health Care Management, Incorporated, 2006.

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Felgen, Jayne. I2e2: Leading Lasting Change. Creative Health Care Management, Incorporated, 2013.

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A historical introduction to the law of obligations. Oxford University Press, 1999.

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A historical introduction to the law of obligations. Oxford University Press, 2001.

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Book chapters on the topic "I2b2"

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Segagni, Daniele, Matteo Gabetta, Valentina Tibollo, Alberto Zambelli, Silvia G. Priori, and Riccardo Bellazzi. "ONCO-i2b2: Improve Patients Selection through Case-Based Information Retrieval Techniques." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31040-9_10.

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Hong, Na, Zheng Li, Richard C. Kiefer, et al. "Building an i2b2-Based Integrated Data Repository for Cancer Research: A Case Study of Ovarian Cancer Registry." In Data Management and Analytics for Medicine and Healthcare. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57741-8_8.

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Cascio, Salvatore, Stuart J. O’Toole, and Hock Lim Tan. "I22 Retroperitoneoscopic Nephrectomy." In Basic Techniques in Pediatric Surgery. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-20641-2_180.

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Jain, M., and A. Gupta. "2601 Diamagnetic susceptibility of I2S2." In Diamagnetic Susceptibility and Anisotropy of Inorganic and Organometallic Compounds. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-44694-1_2602.

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Li, Yi, Nicola Stokes, Lawrence Cavedon, and Alistair Moffat. "NICTA I2D2 Group at GeoCLEF 2006." In Evaluation of Multilingual and Multi-modal Information Retrieval. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_120.

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Salén, Peter, Ming Liu, and Peter van der Meulen. "Femtosecond Photo-induced Dissociation of the Trihalide Anions I3 − and I2Br− in Solution." In Springer Series in Chemical Physics. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-27213-5_152.

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Majumdar, Joydeep, Ram S. Mohril, Bhupesh K. Lad, and Makarand S. Kulkarni. "Re-imagining Military Logistics—Reliability, Availability, Maintainability and Safety (ML-RAMS) with Intelligent, Interconnected, Digital and Distributed (I2D2) Technological Framework." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3087-2_42.

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Zhao, Qianchuan, and Ziyan Jiang. "Insect Intelligent Building (I2B): A New Architecture of Building Control Systems Based on Internet of Things (IoT)." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6733-5_42.

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Bucalo, Mauro, Matteo Gabetta, Lorenzo Chiudinelli, et al. "i2b2 to Optimize Patients Enrollment." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210217.

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i2b2 data-warehouse could be a useful tool to support the enrollment phase of clinical studies. The aim of this work is to evaluate its performance on two clinical trials. We developed also an i2b2 extension to help in suggesting eligible patients for a study. The work showed good results in terms of ability to implement inclusion/exclusion criteria, but also in terms of identified patients actually enrolled and high number of patients suggested as potentially enrollable.
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Majeed, Raphael W., Patrick Fischer, and Andreas Günther. "Accessing OMOP Common Data Model Repositories with the i2b2 Webclient – Algorithm for Automatic Query Translation." In German Medical Data Sciences: Bringing Data to Life. IOS Press, 2021. http://dx.doi.org/10.3233/shti210077.

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In the era of translational research, data integration and clinical data warehouses are important enabling technologies for clinical researchers. The OMOP common data model is a wide-spread choice as a target for data integration in medical informatics. It’s portability of queries and analyses across different institutions and data are ideal also from the viewpoint of the FAIR principles. Yet, the OMOP CDM lacks a simple and intuitive user interface for untrained users to run simple queries for feasibility analysis. Aim of this study is to provide an algorithm to translate any given i2b2 query to an equivalent query which can then be run on the OMOP CDM database. The provided algorithm is able to convert queries created in the i2b2 webclient to SQL statements which can be executed on a standard OMOP CDM database programmatically.
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Conference papers on the topic "I2b2"

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London, J. W., and D. Chatterjee. "Implications of observation-fact modifiers to i2b2 ontologies." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112507.

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Harris, Daniel R., Adam D. Baus, Tamela J. Harper, Traci D. Jarrett, Cecil R. Pollard, and Jeffery C. Talbert. "Using i2b2 to bootstrap rural health analytics and learning networks." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591246.

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Sharma, Himanshu, Chengsheng Mao, Yizhen Zhang, et al. "Portable Phenotyping System: A Portable Machine-Learning Approach to i2b2 Obesity Challenge." In 2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W). IEEE, 2018. http://dx.doi.org/10.1109/ichi-w.2018.00032.

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Yip, Vincent, and Umit Topaloglu. "Concept integration from the caTIES to i2b2 using the UMLS semantic network." In the ACM international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1882992.1883044.

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Gallagher, Carla J. "Use of the Informatics for Integrating Biology and the Bedside (i2b2) Population to Test Serum Bilirubin Levels and Risk for Inflammatory Bowl Diseases and the Involvement of Uridine Glucuronosyltransferase Genes." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2018. http://dx.doi.org/10.1145/3233547.3233638.

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Tremper, David, and Andy Brosky. "Upgraded immersive input display device (I2D2)." In Defense and Security Symposium, edited by John T. Thomas and Andrew Malloy. SPIE, 2007. http://dx.doi.org/10.1117/12.720536.

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Tremper, David E., Kevin P. Burnett, Andrew R. Malloy, and Robert Wert. "Immersive Input Display Device (I2D2) for tactical information viewing." In Defense and Security Symposium, edited by Randall W. Brown, Peter L. Marasco, Clarence E. Rash, and Colin E. Reese. SPIE, 2006. http://dx.doi.org/10.1117/12.670413.

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Bhagavatula, Chandra, Jena D. Hwang, Doug Downey, et al. "I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.535.

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Maierhofer, Christiane, and Mathias Roellig. "I2.2 - Application of active thermography to the detection of safety relevant defects in civil engineering structures." In SENSOR+TEST Conferences 2009. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2009. http://dx.doi.org/10.5162/irs09/i2.2.

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Forg, B., F. Herrmann, W. Leneke, et al. "2.2 - Thermopile Sensor Array with Improved Spatial Resolu¬tion, Sensitivity and Image quality." In SENSOR+TEST Conferences 2011. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2011. http://dx.doi.org/10.5162/irs11/i2.2.

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