Bibliographies thématiques / Temporal clinical data warehouse

Littérature scientifique sur le sujet « Temporal clinical data warehouse »

Auteur : Grafiati
Publié le 11 mars 2023

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Articles de revues sur le sujet "Temporal clinical data warehouse"

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Garani, Georgia, et Canan Eren Atay. « Encountering Incomplete Temporal Information in Clinical Data Warehouses ». International Journal of Applied Research on Public Health Management 5, no 1 (janvier 2020) : 32–48. http://dx.doi.org/10.4018/ijarphm.2020010103.

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A clinical data warehouse (CDW) can be an important tool for the purposes of analysis and critical decision making in the medical field. Such a data repository integrates heterogeneous health data, including clinical, treatment and diagnostic data and laboratory test results from a variety of sources. Accurate data need to be stored and processed in a CDW with adequate computation capabilities and thus, time plays a crucial factor. A slowly changing dimension (SCD) is a dimension that changes slowly over time, either gradually or intermittently. This article introduces a new SCD type, Type BTA, where both valid time and transaction time are supported for providing a complete history of the dimensional data. With Type BTA, the history of an object can be captured through the changes as reflected in the CDW. Consequently, for the first time, the full history of retroactive and post-active changes can be preserved in a CDW. Specifically, Type BTA is implemented for a Clinical Data Warehouse using real cancer data, for which the advantages of this methodology are demonstrated and advocated.
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Looten, Vincent, Liliane Kong Win Chang, Antoine Neuraz, Marie-Anne Landau-Loriot, Benoit Vedie, Jean-Louis Paul, Laëtitia Mauge et al. « What can millions of laboratory test results tell us about the temporal aspect of data quality ? Study of data spanning 17 years in a clinical data warehouse ». Computer Methods and Programs in Biomedicine 181 (novembre 2019) : 104825. http://dx.doi.org/10.1016/j.cmpb.2018.12.030.

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Dagliati, Arianna, Lucia Sacchi, Valentina Tibollo, Giulia Cogni, Marsida Teliti, Antonio Martinez-Millana, Vicente Traver et al. « A dashboard-based system for supporting diabetes care ». Journal of the American Medical Informatics Association 25, no 5 (2 février 2018) : 538–47. http://dx.doi.org/10.1093/jamia/ocx159.

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Abstract Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system’s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.
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Taweel, A., S. Miles, B. C. Delaney et R. Bache. « An Eligibility Criteria Query Language for Heterogeneous Data Warehouses ». Methods of Information in Medicine 54, no 01 (2015) : 41–44. http://dx.doi.org/10.3414/me13-02-0027.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Objectives: The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher- level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources.Methods: By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content.Results: The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous.Discussion: An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations.Conclusions: Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.
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Suzuki, Hiroyuki, Eli Perencevich, Daniel Diekema, Daniel Livorsi, Marin Schweizer, Rajeshwari Nair, Michael Ohl et al. « 1031. Nationwide Temporal Trends of Candidemia Incidence Over 18 Years Within the Veteran Health Administration System ». Open Forum Infectious Diseases 5, suppl_1 (novembre 2018) : S307. http://dx.doi.org/10.1093/ofid/ofy210.868.

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Abstract Background Bloodstream infection due to Candida spp. is common and associated with significant mortality and morbidity. Previous population-based studies in 2000s and early 2010s have suggested that the incidence of candidemia might be increasing, presumably due to widespread use of central lines and broad-spectrum antibiotics. However, recent trends of candidemia incidence have not been not well described. Methods We conducted a retrospective cohort study of all veterans cared for in the Veterans Health Administration (VHA) system from January 2000 to December 2017 to determine the incidence of candidemia. All patients who had positive blood cultures were identified using data available in the electronic medical record data warehouse, and the number of unique patients for each month was calculated. Patient-days was used as a denominator, and the incidence rate was expressed as the number of unique patients with candidemia per patient-days for each month. Temporal trends were analyzed by joinpoint regression models to identify statistically significant changes in trend. Results Over the study period, 31,370 positive blood cultures for Candida spp. from 15,763 unique patients were identified. The mean monthly incidence rate was 22.5 per 100,000 patient-days (IQR: 15.6–28.4). Incidence rates were increasing in the early 2000s and relatively stable in the mid-2000s, followed by a sustained decline (figure). Joinpoint regression analysis revealed there were two statistically significant changes in slope, one in September 2003 (95% CI: 2/2002–1/2005) and another in 6/2007 (95% CI: 4/2006–3/2009). Conclusion In the VHA system, there were significant changes in temporal trends of candidemia incidence rates over 18 years, including a substantial increase in the early 2000s followed by a sustained decline in later years. The incidence rates during 2016–2017 were nearly one-third of their peak in the mid-2000s. Possible explanations for the sustained decline include prevention efforts for healthcare-associated infections, such as central-line associated bloodstream infections. Further study is needed to investigate etiologies of these changes in temporal trends to identify potential effective prevention for candidemia. Disclosures M. Ohl, Gilead Sciences, Inc.: Grant Investigator, Research grant
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Lee, Kyeryoung, Zongzhi Liu, Meng Ma, Yun Mai, Christopher Gilman, Minghao Li, Mingwei Zhang et al. « Analyzing treatment patterns and time to the next treatment in chronic lymphocytic leukemia real-world data using automated temporal phenotyping. » Journal of Clinical Oncology 39, no 15_suppl (20 mai 2021) : e19512-e19512. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e19512.

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e19512 Background: Targeted therapy is an important treatment for chronic lymphocytic leukemia (CLL). However, optimal strategies for deploying small molecule inhibitors or antibody therapies in the real world are not well understood, largely due to a lack of outcomes data. We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. Here, the CLL treatment pattern and time to the next treatment (TTNT) were analyzed in real-world data (RWD) using patient electronic health records. Methods: We identified a CLL cohort with LOT from the Mount Sinai Data Warehouse (2003-2020). Each LOT consisted of either a single agent or combinations defined by NCCN CLL guidelines. We developed a natural language processing (NLP)-based temporal phenotyping approach to automatically identify the number of lines and therapeutic regimens. The sequence of treatment and time interval for each patient were derived from the systematic treatment data. Time to event analysis and multivariate (i.e., age, gender, race, other treatment patterns) Cox proportional hazard (CoxPH) models were used to analyze the patterns and predictors of TTNT. Results: Four hundred eleven CLL patients received 1 to 7 LOTs. Ibrutinib was the predominant 1st LOT (40.8% of patients) followed by anti-CD20-based antibody therapies and chemotherapy in 30.6 and 19.2% of patients, respectively, followed by Acalabrutinib, Venetoclax, and Idelalisib in 3.4, 2.7, and 0.7% of patients, respectively (Table 1). The 2nd to 5th LOT showed the same or similar trends. We next analyzed the TTNT in the 1st line of each therapeutic class. Acalabrutinib resulted in a longer median TTNT than Ibrutinib. Both Acalabrutinib and Ibrutinib showed longer TTNT compared to Venetoclax (median TTNTs were 742 and 598 vs. 373 days: HR = 0.23, p=0.015 and HR = 0.48, p=0.03, respectively). In addition, patients with age equal to or older than 65 showed longer TNNT (HR=0.16, p=0.016). Conclusions: Our result shows the potential of RWD usage in clinical decision making as real-world evidence reported here is consistent with results derived from clinical trial data. Linking this study to genetic data and other covariates affecting treatment outcomes may provide additional insights into the optimal sequences of the targeted therapies in CLL. Table 1: Therapeutic class and patient numbers (%) in each line.[Table: see text]
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Ross, Mindy K., Henry Zheng, Bing Zhu, Ailina Lao, Hyejin Hong, Alamelu Natesan, Melina Radparvar et Alex A. T. Bui. « Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution ». Methods of Information in Medicine 59, no 06 (décembre 2020) : 219–26. http://dx.doi.org/10.1055/s-0041-1729951.

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Abstract Objectives Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., International Classification of Disease, ICD) in addition to other criteria (e.g., inhaler medications)—but presume an accurate diagnosis. As such, there is no universally accepted or rigorously tested computable phenotype for asthma. Methods We compared two established asthma computable phenotypes: the Chicago Area Patient-Outcomes Research Network (CAPriCORN) and Phenotype KnowledgeBase (PheKB). We established a large-scale, consensus gold standard (n = 1,365) from the University of California, Los Angeles Health System's clinical data warehouse for patients 5 to 17 years old. Results were manually reviewed and predictive performance (positive predictive value [PPV], sensitivity/specificity, F1-score) determined. We then examined the classification errors to gain insight for future algorithm optimizations. Results As applied to our final cohort of 1,365 expert-defined gold standard patients, the CAPriCORN algorithms performed with a balanced PPV = 95.8% (95% CI: 94.4–97.2%), sensitivity = 85.7% (95% CI: 83.9–87.5%), and harmonized F1 = 90.4% (95% CI: 89.2–91.7%). The PheKB algorithm was performed with a balanced PPV = 83.1% (95% CI: 80.5–85.7%), sensitivity = 69.4% (95% CI: 66.3–72.5%), and F1 = 75.4% (95% CI: 73.1–77.8%). Four categories of errors were identified related to method limitations, disease definition, human error, and design implementation. Conclusion The performance of the CAPriCORN and PheKB algorithms was lower than previously reported as applied to pediatric data (PPV = 97.7 and 96%, respectively). There is room to improve the performance of current methods, including targeted use of natural language processing and clinical feature engineering.
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Morgan, Ethan, Sam Hohmann, Jessica P. Ridgway, Robert S. Daum et Michael Z. David. « Decreasing Incidence of Skin and Soft-tissue Infections in 86 US Emergency Departments, 2009–2014 ». Clinical Infectious Diseases 68, no 3 (15 juin 2018) : 453–59. http://dx.doi.org/10.1093/cid/ciy509.

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Abstract Background The incidence of skin and soft-tissue infections (SSTIs), for which human immunodeficiency virus (HIV) is a significant risk factor, in United States emergency departments (EDs) increased dramatically after 2000 with the emergence of community-associated methicillin-resistant Staphylococcus aureus. Few studies have examined SSTI incidence among HIV-infected and non–HIV-infected patients in the United States after 2010. Methods Data were obtained for patient encounters at all academic medical center EDs affiliated with the Vizient clinical data warehouse assigned an SSTI-associated code based on the International Classification of Diseases, Ninth Revision, between 1 January 2009 and 31 December 2014. The rate was calculated per 1000 ED encounters by year and stratified by SSTI, HIV infection, or both, and by age group, race, payer type, and region of care. Poisson regression was used to assess temporal change over the study period. Results In 2009–2014, a total of 47317 HIV-associated and 820440 SSTI-associated encounters were recorded among 25239781 ED patient encounters. The rate of SSTIs decreased by 8% among all patients and by 14.6%, among those with HIV infection. The SSTI incidence overall decreased from 32.0 to 29.7 per 1000 ED encounters between 2009 and 2014. HIV-infected patients had a significantly higher rate of SSTIs than HIV-uninfected patients (adjusted rate ratio, 1.91; 95% confidence interval, 1.84–1.99). Conclusions The decline in SSTI incidence in US EDs between 2009 and 2014 is a remarkable epidemiologic shift from the increase in SSTIs after 2000, and further research is necessary to assess reasons for this decrease.
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Ma, Meng, Arielle Redfern, Xiang Zhou, Dan Li, Ying Ru, Kyeryoung Lee, Christopher Gilman et al. « Automated abstraction of real-world clinical outcome in lung cancer : A natural language processing and artificial intelligence approach from electronic health records. » Journal of Clinical Oncology 38, no 15_suppl (20 mai 2020) : e14062-e14062. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14062.

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e14062 Background: Real world evidence generated from electronic health records (EHRs) is playing an increasing role in health care decisions. It has been recognized as an essential element to assess cancer outcomes in real-world settings. Automatically abstracting outcomes from notes is becoming a fundamental challenge in medical informatics. In this study, we aim to develop a system to automatically abstract outcomes (Progression, Response, Stable Disease) from notes in lung cancer. Methods: A lung cancer cohort (n = 5,003) was obtained from the Mount Sinai Data Warehouse. The progress, pathology and radiology notes of patients were used. We integrated various techniques of Natural Language Processing (NLP) and Artificial Intelligence (AI) and developed a system to automatically abstract outcomes. The corresponding images, biopsies and lines of treatments (LOTs) were abstracted as attributes of outcomes. This system includes four information models: 1. Customized NLP annotator model: preprocessor, section detector, sentence splitter, named entity recognition, relation detector; CRF and LSTM methods were applied to recognize entities and relations. 2. Clinical Outcome container model: biopsy evidence extractor, lines of treatment detector, image evidence extractor, clinical outcome event recognizer, date detector, and temporal reasoning; Domain-specific rules were crafted to automatically infer outcomes. 3. Document Summarizer; 4. Longitudinal Outcome Summarizer. Results: To evaluate the outcomes abstracted, we curated a subset (n = 792) from patient cohort for which LOTs were available. About 61% of the outcomes identified were supported by radiologic images (time window = ±14 days) or biopsy pathology results (time window = ±100 days). In 91% (720/792) of patients, Progression was abstracted within a time window of 90 days prior to first-line treatment. Also, 72% of the Progression events identified were accompanied by a downstream event (e.g., treatment change or death). We randomly selected 250 outcomes for manual curation, and 197 outcomes were assessed to be correct (precision = 79%). Moreover, our automated abstraction system improved human abstractor efficiency to curate outcomes, reducing curation time per patient by 90%. Conclusions: We have demonstrated the feasibility and effectiveness of NLP and AI approaches to abstract outcomes from lung cancer EHR data. It promises to automatically abstract outcomes and other clinical entities from notes across all cancers.
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Garani, Georgia, George K. Adam et Dimitrios Ventzas. « Temporal data warehouse logical modelling ». International Journal of Data Mining, Modelling and Management 8, no 2 (2016) : 144. http://dx.doi.org/10.1504/ijdmmm.2016.077156.

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Thèses sur le sujet "Temporal clinical data warehouse"

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Jamjoom, Arwa. « Transitioning a clinical unit to a data warehouse ». Thesis, University of Surrey, 2011. http://epubs.surrey.ac.uk/804656/.

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Tsuruda, Renata Miwa. « STB-index : um índice baseado em bitmap para data warehouse espaço-temporal ». Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/525.

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Financiadora de Estudos e Projetos
The growing concern with the support of the decision-making process has made companies to search technologies that support their decisions. The technology most widely used presently is the Data Warehouse (DW), which allows storing data so it is possible to produce useful and reliable information to assist in strategic decisions. Combining the concepts of Spatial Data Warehouse (SDW), that allows geometry storage and managing, and Temporal Data Warehouse (TDW), which allows storing data changes that occur in the real-world, a research topic known as Spatio-Temporal Data Warehouse (STDW) has emerged. STDW are suitable for the treatment of geometries that change over time. These technologies, combined with the steady growth volume of data, show the necessity of index structures to improve the performance of analytical query processing with spatial predicates and also with geometries that may vary over time. In this sense, this work focused on proposing an index for STDW called Spatio-Temporal Bitmap Index, or STB-index. The proposed index was designed to processing drill-down and roll-up queries considering the existence of predefined spatial hierarchies and with spatial attributes that can vary its position and shape over time. The validation of STB-index was performed by conducting experimental tests using a DWET created from synthetic data. Tests evaluated the elapsed time and the number of disk accesses to construct the index, the amount of storage space of the index and the elapsed time and the number of disk accesses for query processing. Results were compared with query processing using database management system resources and STBindex improved the query performance by 98.12% up to 99.22% in response time compared to materialized views.
A crescente preocupação com o suporte ao processo de tomada de decisão estratégica fez com que as empresas buscassem tecnologias que apoiassem as suas decisões. A tecnologia mais utilizada atualmente é a de Data Warehouse (DW), que permite armazenar dados de forma que seja possível produzir informação útil e confiável para auxiliar na tomada de decisão estratégica. Aliando-se os conceitos de Data Warehouse Espacial (DWE), que permite o armazenamento e o gerenciamento de geometrias, e de Data Warehouse Temporal (DWT), que possibilita representar as mudanças nos dados que ocorrem no mundo real, surgiu o tema de pesquisa conhecido por Data Warehouse Espaço-Temporal (DWET), que é próprio para o tratamento de geometrias que se alteram ao longo do tempo. Essas tecnologias, aliadas ao constante crescimento no volume de dados armazenados, evidenciam a necessidade de estruturas de indexação que melhorem o desempenho do processamento de consultas analíticas com predicados espaciais e com variação das geometrias no tempo. Nesse sentido, este trabalho se concentrou na proposta de um índice para DWET denominado Spatio- Temporal Bitmap Index, ou STB-index. O índice proposto foi projetado para o processamento de consultas do tipo drill-down e roll-up considerando a existência de hierarquias espaciais predefinidas, sendo que os atributos espaciais podem variar sua posição e sua forma ao longo do tempo. A validação do STB-index ocorreu por meio da realização de testes experimentais utilizando um DWET criado a partir de dados sintéticos. Os testes avaliaram o tempo e o número de acessos a disco para a construção do índice, a quantidade de espaço para armazenamento do índice e o tempo e número de acessos a disco para o processamento de consultas analíticas. Os resultados obtidos foram comparados com o processamento de consultas utilizando os recursos disponíveis dos sistemas gerenciadores de banco de dados, sendo que o STB-index apresentou um ganho de desempenho entre 98,12% e 99,22% no tempo de resposta das consultas se comparado ao uso de visões materializadas.
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Veronica, Ruiz Castro Carla. « CSTM : a conceptual spatiotemporal model for data warehouses ». Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/2209.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico
Estudos abrangentes relacionados a data warehouse temporais e espaciais têm sido conduzidos. Data warehouse temporais permitem lidar com dados variáveis no tempo tanto em tabelas de fatos quanto em tabelas de dimensões. Uma ampla variedade de aplicações precisa capturar não só características espaciais, mas também temporais das entidades modeladas. Entretanto, estudos que unam essas duas áreas de pesquisa não têm sido suficientemente considerados. É neste contexto que o presente trabalho de dissertação está definido. Ele propõe um modelo conceitual para data warehouses espaço temporais. Este modelo permite aos usuários definir níveis, hierarquias e dimensões tanto com características espaciais como temporais. Como consequência disso, é possível representar atributos espaciais variáveis no tempo. Além disso, este trabalho define um conjunto de operadores espaço temporais que poderia ser útil na consulta de data warehouses espaço temporais. Diferentemente de propostas existentes, nossos operadores integram não só operadores multidimensionais e espaciais, mas também espaciais e temporais (i.e., espaço temporais) em uma única sintaxe. Um esquema taxonômico, o qual classifica os operadores propostos, também é definido. A importância da taxonomia proposta é que ajuda no desenvolvimento de tecnologia OLAP espaço temporal. Com o objetivo de automatizar a modelagem de esquemas espaço temporais, uma ferramenta CASE foi desenvolvida. Além de permitir a definição de esquemas conformes com o modelo conceitual proposto, esta ferramenta também permite a geração automática do esquema lógico correspondente usando uma abordagem objeto relacional. As ideias propostas são validadas com um estudo de caso na área meteorológica. O estudo apresenta uma aplicação prática do modelo conceitual espaço temporal e dos operadores espaço temporais apresentados neste trabalho
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Filannino, Michele. « Data-driven temporal information extraction with applications in general and clinical domains ». Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/datadriven-temporal-information-extraction-with-applications-in-general-and-clinical-domains(34d7e698-f8a8-4fbf-b742-d522c4fe4a12).html.

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The automatic extraction of temporal information from written texts is pivotal for many Natural Language Processing applications such as question answering, text summarisation and information retrieval. However, Temporal Information Extraction (TIE) is a challenging task because of the amount of types of expressions (durations, frequencies, times, dates) and their high morphological variability and ambiguity. As far as the approaches are concerned, the most common among the existing ones is rule-based, while data-driven ones are under-explored. This thesis introduces a novel domain-independent data-driven TIE strategy. The identification strategy is based on machine learning sequence labelling classifiers on features selected through an extensive exploration. Results are further optimised using an a posteriori label-adjustment pipeline. The normalisation strategy is rule-based and builds on a pre-existing system. The methodology has been applied to both specific (clinical) and generic domain, and has been officially benchmarked at the i2b2/2012 and TempEval-3 challenges, ranking respectively 3rd and 1st. The results prove the TIE task to be more challenging in the clinical domain (overall accuracy 63%) rather than in the general domain (overall accuracy 69%).Finally, this thesis also presents two applications of TIE. One of them introduces the concept of temporal footprint of a Wikipedia article, and uses it to mine the life span of persons. In the other case, TIE techniques are used to improve pre-existing information retrieval systems by filtering out temporally irrelevant results.
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Mawilmada, Pubudika Kumari. « Impact of a data warehouse model for improved decision-making process in healthcare ». Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/47532/1/Pubudika_Mawilmada_Thesis.pdf.

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The health system is one sector dealing with a deluge of complex data. Many healthcare organisations struggle to utilise these volumes of health data effectively and efficiently. Also, there are many healthcare organisations, which still have stand-alone systems, not integrated for management of information and decision-making. This shows, there is a need for an effective system to capture, collate and distribute this health data. Therefore, implementing the data warehouse concept in healthcare is potentially one of the solutions to integrate health data. Data warehousing has been used to support business intelligence and decision-making in many other sectors such as the engineering, defence and retail sectors. The research problem that is going to be addressed is, "how can data warehousing assist the decision-making process in healthcare". To address this problem the researcher has narrowed an investigation focusing on a cardiac surgery unit. This research used the cardiac surgery unit at the Prince Charles Hospital (TPCH) as the case study. The cardiac surgery unit at TPCH uses a stand-alone database of patient clinical data, which supports clinical audit, service management and research functions. However, much of the time, the interaction between the cardiac surgery unit information system with other units is minimal. There is a limited and basic two-way interaction with other clinical and administrative databases at TPCH which support decision-making processes. The aims of this research are to investigate what decision-making issues are faced by the healthcare professionals with the current information systems and how decision-making might be improved within this healthcare setting by implementing an aligned data warehouse model or models. As a part of the research the researcher will propose and develop a suitable data warehouse prototype based on the cardiac surgery unit needs and integrating the Intensive Care Unit database, Clinical Costing unit database (Transition II) and Quality and Safety unit database [electronic discharge summary (e-DS)]. The goal is to improve the current decision-making processes. The main objectives of this research are to improve access to integrated clinical and financial data, providing potentially better information for decision-making for both improved from the questionnaire and by referring to the literature, the results indicate a centralised data warehouse model for the cardiac surgery unit at this stage. A centralised data warehouse model addresses current needs and can also be upgraded to an enterprise wide warehouse model or federated data warehouse model as discussed in the many consulted publications. The data warehouse prototype was able to be developed using SAS enterprise data integration studio 4.2 and the data was analysed using SAS enterprise edition 4.3. In the final stage, the data warehouse prototype was evaluated by collecting feedback from the end users. This was achieved by using output created from the data warehouse prototype as examples of the data desired and possible in a data warehouse environment. According to the feedback collected from the end users, implementation of a data warehouse was seen to be a useful tool to inform management options, provide a more complete representation of factors related to a decision scenario and potentially reduce information product development time. However, there are many constraints exist in this research. For example the technical issues such as data incompatibilities, integration of the cardiac surgery database and e-DS database servers and also, Queensland Health information restrictions (Queensland Health information related policies, patient data confidentiality and ethics requirements), limited availability of support from IT technical staff and time restrictions. These factors have influenced the process for the warehouse model development, necessitating an incremental approach. This highlights the presence of many practical barriers to data warehousing and integration at the clinical service level. Limitations included the use of a small convenience sample of survey respondents, and a single site case report study design. As mentioned previously, the proposed data warehouse is a prototype and was developed using only four database repositories. Despite this constraint, the research demonstrates that by implementing a data warehouse at the service level, decision-making is supported and data quality issues related to access and availability can be reduced, providing many benefits. Output reports produced from the data warehouse prototype demonstrated usefulness for the improvement of decision-making in the management of clinical services, and quality and safety monitoring for better clinical care. However, in the future, the centralised model selected can be upgraded to an enterprise wide architecture by integrating with additional hospital units’ databases.
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Dietrich, Georg [Verfasser], et Frank [Gutachter] Puppe. « Ad Hoc Information Extraction in a Clinical Data Warehouse with Case Studies for Data Exploration and Consistency Checks / Georg Dietrich ; Gutachter : Frank Puppe ». Würzburg : Universität Würzburg, 2019. http://d-nb.info/1191102610/34.

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Koylu, Caglar. « A Case Study In Weather Pattern Searching Using A Spatial Data Warehouse Model ». Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609573/index.pdf.

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Data warehousing and Online Analytical Processing (OLAP) technology has been used to access, visualize and analyze multidimensional, aggregated, and summarized data. Large part of data contains spatial components. Thus, these spatial components convey valuable information and must be included in exploration and analysis phases of a spatial decision support system (SDSS). On the other hand, Geographic Information Systems (GISs) provide a wide range of tools to analyze spatial phenomena and therefore must be included in the analysis phases of a decision support system (DSS). In this regard, this study aims to search for answers to the problem how to design a spatially enabled data warehouse architecture in order to support spatio-temporal data analysis and exploration of multidimensional data. Consequently, in this study, the concepts of OLAP and GISs are synthesized in an integrated fashion to maximize the benefits generated from the strengths of both systems by building a spatial data warehouse model. In this context, a multidimensional spatio-temporal data model is proposed as a result of this synthesis. This model addresses the integration problem of spatial, non-spatial and temporal data and facilitates spatial data exploration and analysis. The model is evaluated by implementing a case study in weather pattern searching.
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Hagen, Matthew. « Biological and clinical data integration and its applications in healthcare ». Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54267.

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Answers to the most complex biological questions are rarely determined solely from the experimental evidence. It requires subsequent analysis of many data sources that are often heterogeneous. Most biological data repositories focus on providing only one particular type of data, such as sequences, molecular interactions, protein structure, or gene expression. In many cases, it is required for researchers to visit several different databases to answer one scientific question. It is essential to develop strategies to integrate disparate biological data sources that are efficient and seamless to facilitate the discovery of novel associations and validate existing hypotheses. This thesis presents the design and development of different integration strategies of biological and clinical systems. The BioSPIDA system is a data warehousing solution that integrates many NCBI databases and other biological sources on protein sequences, protein domains, and biological pathways. It utilizes a universal parser facilitating integration without developing separate source code for each data site. This enables users to execute fine-grained queries that can filter genes by their protein interactions, gene expressions, functional annotation, and protein domain representation. Relational databases can powerfully return and generate quickly filtered results to research questions, but they are not the most suitable solution in all cases. Clinical patients and genes are typically annotated by concepts in hierarchical ontologies and performance of relational databases are weakened considerably when traversing and representing graph structures. This thesis illustrates when relational databases are most suitable as well as comparing the performance benchmarks of semantic web technologies and graph databases when comparing ontological concepts. Several approaches of analyzing integrated data will be discussed to demonstrate the advantages over dependencies on remote data centers. Intensive Care Patients are prioritized by their length of stay and their severity class is estimated by their diagnosis to help minimize wait time and preferentially treat patients by their condition. In a separate study, semantic clustering of patients is conducted by integrating a clinical database and a medical ontology to help identify multi-morbidity patterns. In the biological area, gene pathways, protein interaction networks, and functional annotation are integrated to help predict and prioritize candidate disease genes. This thesis will present the results that were able to be generated from each project through utilizing a local repository of genes, functional annotations, protein interactions, clinical patients, and medical ontologies.
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Lluch-Ariet, Magí. « Contributions to efficient and secure exchange of networked clinical data : the MOSAIC system ». Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/388037.

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The understanding of certain data often requires the collection of similar data from different places to be analysed and interpreted. Multi-agent systems (MAS), interoperability standards (DICOM, HL7 or EN13606) and clinical Ontologies, are facilitating data interchange among different clinical centres around the world. However, as more and more data becomes available, and more heterogeneous this data gets, the task of accessing and exploiting the large number of distributed repositories to extract useful knowledge becomes increasingly complex. Beyond the existing networks and advances for data transfer, data sharing protocols to support multilateral agreements are useful to exploit the knowledge of distributed Data Warehouses. The access to a certain data set in a federated Data Warehouse may be constrained by the requirement to deliver another specific data set. When bilateral agreements between two nodes of a network are not enough to solve the constraints for accessing to a certain data set, multilateral agreements for data exchange can be a solution. The research carried out in this PhD Thesis comprises the design and implementation of a Multi-Agent System for multilateral exchange agreements of clinical data, and evaluate how those multilateral agreements increase the percentage of data collected by a single node from the total amount of data available in the network. Different strategies to reduce the number of messages needed to achieve an agreement are also considered. The results show that with this collaborative sharing scenario the percentage of data collected dramatically improve from bilateral agreements to multilateral ones, up to reach almost all data available in the network.
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Scheufele, Elisabeth Lee. « Medication recommendations vs. peer practice in pediatric levothyroxine dosing : a study of collective intelligence from a clinical data warehouse as a potential model for clinical decision support ». Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47854.

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Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.
Includes bibliographical references.
Clinical decision support systems (CDSS) are developed primarily from knowledge gleaned from evidence-based research, guidelines, trusted resources and domain experts. While these resources generally represent information that is research proven, time-tested and consistent with current medical knowledge, they lack some qualities that would be desirable in a CDSS. For instance, the information is presented as generalized recommendations that are not specific to particular patients and may not consider certain subpopulations. In addition, the knowledge base that produces the guidelines may be outdated and may not reflect real-world practice. Ideally, resources for decision support should be timely, patient-specific, and represent current practice. Patient-oriented clinical decision support is particularly important in the practice of pediatrics because it addresses a population in constant flux. Every age represents a different set of physiological and developmental concerns and considerations, especially in medication dosing patterns. Patient clinical data warehouses (CDW) may be able to bridge the knowledge gap. CDWs contain the collective intelligence of various contributors (i.e. clinicians, administrators, etc.) where each data entry provides information regarding medical care for a patient in the real world. CDWs have the potential to provide information as current as the latest upload, be focused to specific subpopulations and reflect current clinical practice. In this paper, I study the potential of a well-known patient clinical data warehouse to provide information regarding pediatric levothyroxine dosing as a form of clinical decision support. I study the state of the stored data, the necessary data transformations and options for representing the data to effectively summarize and communicate the findings.
(cont.) I also compare the resulting transformed data, representing actual practice within this population, against established dosing recommendations. Of the transformed records, 728 of the 854 (85.2%, [95% confidence interval 82.7:87.6]) medication records contained doses that were under the published recommended range for levothyroxine. As demonstrated by these results, real world practice can diverge from established recommendations. Delivering this information on real-world peer practice medication dosing to clinicians in real-time offers the potential to provide a valuable supplement to established dosing guidelines, enhancing the general and sometimes static dosing recommendations.
by Elisabeth Lee Scheufele.
S.M.
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Livres sur le sujet "Temporal clinical data warehouse"

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Zimányi, Esteban, et Elzbieta Malinowski. Advanced Data Warehouse Design : From Conventional to Spatial and Temporal Applications. Springer, 2010.

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Zimányi, Esteban, et Elzbieta Malinowski. Advanced Data Warehouse Design : From Conventional to Spatial and Temporal Applications. Springer London, Limited, 2008.

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Advanced Data Warehouse Design From Conventional To Spatial And Temporal Applications. Springer, 2008.

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Voigt, Jens-Uwe. Quantification of left ventricular function and synchrony using tissue Doppler, strain imaging, and speckle tracking. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199599639.003.0006.

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Modern echocardiographic systems allow the quantitative and qualitative assessment of regional myocardial function by measuring velocity, motion, deformation, and other parameters of myocardial function.Both colour Doppler (CD) and spectral Doppler modes provide one-dimensional estimates of velocity. From CD data only, further parameters can be derived. Tracking techniques have recently been introduced which provide all parameters two-dimensionally, but at the cost of lower temporal resolution.Several clinical applications have been proposed, including regional and global systolic function assessment, evaluation of diastolic cardiac properties, and assessment of ventricular dyssynchrony.This chapter provides an introduction to the method of Doppler- and tracking-based function assessment and provides a basis for understanding its different clinical applications.
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Greenblatt, Samuel H. John Hughlings Jackson. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780192897640.001.0001.

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John Hughlings Jackson (1835–1911) was a preeminent British neurologist in the nineteenth and early twentieth centuries. He began to establish that standing in the 1860s, when he incorporated the evolutionary association psychology of Herbert Spencer into his early analyses of aphasia. Jackson also benefitted from his early connection with the National Hospital, Queen Square, London. His nuanced theory of cerebral localization was derived from (1) his clinical observations of (what Charcot later called) Jacksonian epilepsy, in combination with (2) his innovation to think about neurophysiological events at the cellular level, as well as from (3) David Ferrier’s primate localization data. The result was our modern conception of the seizure focus, which was crucial to the beginnings of modern ‘brain surgery’, especially at the hands of Victor Horsley. Jackson’s influence on the neurophysiology of Charles Sherrington is widely acknowledged but poorly defined. In the larger Victorian culture, Jackson was a friend of George Henry Lewes, who was George Eliot’s companion. Lewes attributed ‘sensibility’ to everything in the nervous system, thus maintaining a monist position on the mind-body relation, whereas Jackson maintained a form of psycho-physical parallelism that was actually dualist (‘Concomitance’). Throughout his life Jackson had an interest in insanity, which he viewed from the point of view of Spencerian evolution and dissolution. The latter was an important component of Freud’s psychoanalysis, which Freud took from Jackson. Late in his life Jackson defined the ‘uncinate group of fits’, which was his version of temporal lobe epilepsy.
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Chapitres de livres sur le sujet "Temporal clinical data warehouse"

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Gorawski, Marcin. « Multiversion Spatio-temporal Telemetric Data Warehouse ». Dans Advances in Databases and Information Systems, 63–70. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12082-4_9.

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Ahmed, Usman, Anne Tchounikine, Maryvonne Miquel et Sylvie Servigne. « Real-Time Temporal Data Warehouse Cubing ». Dans Lecture Notes in Computer Science, 159–67. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15251-1_12.

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Ravat, Franck, et Olivier Teste. « A Temporal Object-Oriented Data Warehouse Model ». Dans Lecture Notes in Computer Science, 583–92. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44469-6_54.

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Śmiałkowska, Bożena. « Effective Methods of Temporal Data Representation in Data Warehouse Systems ». Dans Advanced Computer Systems, 221–34. Boston, MA : Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-8530-9_19.

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Eder, Johann, et Karl Wiggisser. « Modeling Transformations between Versions of a Temporal Data Warehouse ». Dans Advances in Conceptual Modeling – Challenges and Opportunities, 68–77. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87991-6_10.

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Combi, Carlo, Elpida Keravnou-Papailiou et Yuval Shahar. « Abstraction of Time-Oriented Clinical Data ». Dans Temporal Information Systems in Medicine, 139–84. Boston, MA : Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6543-1_5.

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Martin, Carme, et Alberto Abelló. « A Temporal Study of Data Sources to Load a Corporate Data Warehouse ». Dans Data Warehousing and Knowledge Discovery, 109–18. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45228-7_12.

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Ain El Hayat, Soumiya, et Mohamed Bahaj. « A Temporal Data Warehouse Conceptual Modelling and Its Transformation into Temporal Object Relational Model ». Dans Advances in Intelligent Systems and Computing, 314–23. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11928-7_28.

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Combi, Carlo, Elpida Keravnou-Papailiou et Yuval Shahar. « Displaying Time-Oriented Clinical Data and Knowledge ». Dans Temporal Information Systems in Medicine, 301–50. Boston, MA : Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6543-1_8.

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Zhou, Liang, Mingye Bao, Nanhai Yang, Yizhen Lao, Yun Zhang et Yangge Tian. « Spatio-temporal Analysis of Weibo Check-in Data Based on Spatial Data Warehouse ». Dans Geo-Informatics in Resource Management and Sustainable Ecosystem, 466–79. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41908-9_48.

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Actes de conférences sur le sujet "Temporal clinical data warehouse"

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Buyl, Ronald, Marc Nyssen, Martin Žagar, Martin Žagar, Krzysztof Sikora, Antoni Zwiefka, Antoni Zwiefka et al. « Building а National Clinical Data Warehouse ». Dans Fourth International Conference on Telecommunications and Remote Sensing. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005890000930097.

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Turki, Ines Zouari, Faiza Ghozzi Jedidi et Rafik Bouaziz. « Summarizability in Multiversion Data Warehouse ». Dans 2014 21st International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2014. http://dx.doi.org/10.1109/time.2014.12.

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Poenaru, Cristina Elena, Daniel Merezeanu, Radu Dobrescu et Eugenie Posdarascu. « Advanced solutions for medical information storing : Clinical data warehouse ». Dans 2017 E-Health and Bioengineering Conference (EHB). IEEE, 2017. http://dx.doi.org/10.1109/ehb.2017.7995355.

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Nealon, Joshua, Wenny Rahayu et Eric Pardede. « Improving Clinical Data Warehouse Performance via a Windowing Data Structure Architecture ». Dans 2009 International Conference on Computational Science and Its Applications. IEEE, 2009. http://dx.doi.org/10.1109/iccsa.2009.23.

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« MAPPING TEMPORAL DATA WAREHOUSE CONCEPTS TO SAP BW COMPONENTS ». Dans 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002510903910397.

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Tsumoto, Shusaku, et Shoji Hirano. « Multidimensional temporal mining in clinical data ». Dans the 2nd ACM SIGHIT symposium. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2110363.2110426.

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Zhou, Xuezhong, Baoyan Liu, Yinghui Wang, Runsun Zhang, Ping Li, Shibo Chen, Yufeng Guo, Zhuye Gao et Hua Zhang. « Building Clinical Data Warehouse for Traditional Chinese Medicine Knowledge Discovery ». Dans 2008 International Conference on Biomedical Engineering And Informatics (BMEI). IEEE, 2008. http://dx.doi.org/10.1109/bmei.2008.83.

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Savary, L., T. Wan et K. Zeitouni. « Spatio-temporal data warehouse design for human activity pattern analysis ». Dans Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004. IEEE, 2004. http://dx.doi.org/10.1109/dexa.2004.1333576.

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Mantovani, Matteo. « Approximate Temporal Functional Dependencies on Clinical Data ». Dans 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017. http://dx.doi.org/10.1109/ichi.2017.30.

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Puppala, Mamta, Tiancheng He, Xiaohui Yu, Shenyi Chen, Richard Ogunti et Stephen T. C. Wong. « Data security and privacy management in healthcare applications and clinical data warehouse environment ». Dans 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2016. http://dx.doi.org/10.1109/bhi.2016.7455821.

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Rapports d'organisations sur le sujet "Temporal clinical data warehouse"

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Totten, Annette, Dana M. Womack, Marian S. McDonagh, Cynthia Davis-O’Reilly, Jessica C. Griffin, Ian Blazina, Sara Grusing et Nancy Elder. Improving Rural Health Through Telehealth-Guided Provider-to-Provider Communication. Agency for Healthcare Research and Quality, décembre 2022. http://dx.doi.org/10.23970/ahrqepccer254.

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Objectives. To assess the use, effectiveness, and implementation of telehealth-supported provider-to-provider communication and collaboration for the provision of healthcare services to rural populations and to inform a scientific workshop convened by the National Institutes of Health Office of Disease Prevention on October 12–14, 2021. Data sources. We conducted a comprehensive literature search of Ovid MEDLINE®, CINAHL®, Embase®, and Cochrane CENTRAL. We searched for articles published from January 1, 2015, to October 12, 2021, to identify data on use of rural provider-to-provider telehealth (Key Question 1) and the same databases for articles published January 1, 2010, to October 12, 2021, for studies of effectiveness and implementation (Key Questions 2 and 3) and to identify methodological weaknesses in the research (Key Question 4). Additional sources were identified through reference lists, stakeholder suggestions, and responses to a Federal Register notice. Review methods. Our methods followed the Agency for Healthcare Research and Quality Methods Guide (available at https://effectivehealthcare.ahrq.gov/topics/cer-methods-guide/overview) and the PRISMA reporting guidelines. We used predefined criteria and dual review of abstracts and full-text articles to identify research results on (1) regional or national use, (2) effectiveness, (3) barriers and facilitators to implementation, and (4) methodological weakness in studies of provider-to-provider telehealth for rural populations. We assessed the risk of bias of the effectiveness studies using criteria specific to the different study designs and evaluated strength of evidence (SOE) for studies of similar telehealth interventions with similar outcomes. We categorized barriers and facilitators to implementation using the Consolidated Framework for Implementation Research (CFIR) and summarized methodological weaknesses of studies. Results. We included 166 studies reported in 179 publications. Studies on the degree of uptake of provider-to-provider telehealth were limited to specific clinical uses (pharmacy, psychiatry, emergency care, and stroke management) in seven studies using national or regional surveys and claims data. They reported variability across States and regions, but increasing uptake over time. Ninety-seven studies (20 trials and 77 observational studies) evaluated the effectiveness of provider-to-provider telehealth in rural settings, finding that there may be similar rates of transfers and lengths of stay with telehealth for inpatient consultations; similar mortality rates for remote intensive care unit care; similar clinical outcomes and transfer rates for neonates; improvements in medication adherence and treatment response in outpatient care for depression; improvements in some clinical monitoring measures for diabetes with endocrinology or pharmacy outpatient consultations; similar mortality or time to treatment when used to support emergency assessment and management of stroke, heart attack, or chest pain at rural hospitals; and similar rates of appropriate versus inappropriate transfers of critical care and trauma patients with specialist telehealth consultations for rural emergency departments (SOE: low). Studies of telehealth for education and mentoring of rural healthcare providers may result in intended changes in provider behavior and increases in provider knowledge, confidence, and self-efficacy (SOE: low). Patient outcomes were not frequently reported for telehealth provider education, but two studies reported improvement (SOE: low). Evidence for telehealth interventions for other clinical uses and outcomes was insufficient. We identified 67 program evaluations and qualitative studies that identified barriers and facilitators to rural provider-to-provider telehealth. Success was linked to well-functioning technology; sufficient resources, including time, staff, leadership, and equipment; and adequate payment or reimbursement. Some considerations may be unique to implementation of provider-to-provider telehealth in rural areas. These include the need for consultants to better understand the rural context; regional initiatives that pool resources among rural organizations that may not be able to support telehealth individually; and programs that can support care for infrequent as well as frequent clinical situations in rural practices. An assessment of methodological weaknesses found that studies were limited by less rigorous study designs, small sample sizes, and lack of analyses that address risks for bias. A key weakness was that studies did not assess or attempt to adjust for the risk that temporal changes may impact the results in studies that compared outcomes before and after telehealth implementation. Conclusions. While the evidence base is limited, what is available suggests that telehealth supporting provider-to-provider communications and collaboration may be beneficial. Telehealth studies report better patient outcomes in some clinical scenarios (e.g., outpatient care for depression or diabetes, education/mentoring) where telehealth interventions increase access to expertise and high-quality care. In other applications (e.g., inpatient care, emergency care), telehealth results in patient outcomes that are similar to usual care, which may be interpreted as a benefit when the purpose of telehealth is to make equivalent services available locally to rural residents. Most barriers to implementation are common to practice change efforts. Methodological weaknesses stem from weaker study designs, such as before-after studies, and small numbers of participants. The rapid increase in the use of telehealth in response to the Coronavirus disease 2019 (COVID-19) pandemic is likely to produce more data and offer opportunities for more rigorous studies.
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