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Готові списки джерел за темами / Temporal clinical data warehouse / Статті в журналах

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Статті в журналах з теми "Temporal clinical data warehouse"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Garani, Georgia, and Canan Eren Atay. "Encountering Incomplete Temporal Information in Clinical Data Warehouses." International Journal of Applied Research on Public Health Management 5, no. 1 (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

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2

Looten, Vincent, Liliane Kong Win Chang, Antoine Neuraz, 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 (November 2019): 104825. http://dx.doi.org/10.1016/j.cmpb.2018.12.030.

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3

Dagliati, Arianna, Lucia Sacchi, Valentina Tibollo, et al. "A dashboard-based system for supporting diabetes care." Journal of the American Medical Informatics Association 25, no. 5 (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, c

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4

Taweel, A., S. Miles, B. C. Delaney, and 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-repres

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5

Suzuki, Hiroyuki, Eli Perencevich, Daniel Diekema, 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 (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 i

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6

Lee, Kyeryoung, Zongzhi Liu, Meng Ma, 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 (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 wi

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7

Ross, Mindy K., Henry Zheng, Bing Zhu, et al. "Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution." Methods of Information in Medicine 59, no. 06 (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 co

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8

Morgan, Ethan, Sam Hohmann, Jessica P. Ridgway, Robert S. Daum, and Michael Z. David. "Decreasing Incidence of Skin and Soft-tissue Infections in 86 US Emergency Departments, 2009–2014." Clinical Infectious Diseases 68, no. 3 (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-ass

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9

Ma, Meng, Arielle Redfern, Xiang Zhou, 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 (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, path

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10

Garani, Georgia, George K. Adam, and 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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11

Faisal, Sidra, Mansoor Sarwar, Khurram Shahzad, Shahzad Sarwar, Waqar Jaffry, and Muhammad Murtaza Yousaf. "Temporal and Evolving Data Warehouse Design." Scientific Programming 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/7392349.

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The data model of the classical data warehouse (formally, dimensional model) does not offer comprehensive support for temporal data management. The underlying reason is that it requires consideration of several temporal aspects, which involve various time stamps. Also, transactional systems, which serves as a data source for data warehouse, have the tendency to change themselves due to changing business requirements. The classical dimensional model is deficient in handling changes to transaction sources. This has led to the development of various schemes, including evolution of data and evolut

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12

Degoulet, P. "The Virtuous Circles of Clinical Information Systems: a Modern Utopia." Yearbook of Medical Informatics 25, no. 01 (2016): 256–63. http://dx.doi.org/10.15265/iy-2016-030.

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Summary Context: Clinical information systems (CIS) are developed with the aim of improving both the efficiency and the quality of care. Objective: This position paper is based on the hypothesis that such vision is partly a utopian view of the emerging eSociety. Methods: Examples are drawn from 15 years of experience with the fully integrated Georges Pompidou University Hospital (HEGP) CIS and temporal data series extracted from the data warehouses of Assistance Publique - Hôpitaux de Paris (AP-HP) acute care hospitals which share the same administrative organization as HEGP. Three main virtuo

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13

Karami, Mahtab, Azin Rahimi, and Ali Hosseini Shahmirzadi. "Clinical Data Warehouse." Health Care Manager 36, no. 4 (2017): 380–84. http://dx.doi.org/10.1097/hcm.0000000000000113.

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14

Saroha, Kriti, and Anjana Gosain. "Bi-temporal schema versioning in bi-temporal data warehouse." CSI Transactions on ICT 3, no. 2-4 (2015): 135–42. http://dx.doi.org/10.1007/s40012-016-0081-4.

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15

Maiah, Lax, DR A. GOVARDHAN DR.A.GOVARDHAN, and DR C. SUNIL KUMAR. "A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (2013): 146–50. http://dx.doi.org/10.24297/ijct.v4i1c.3114.

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Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the

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16

Atay, Canan Eren, and Georgia Garani. "Maintaining Dimension's History in Data Warehouses Effectively." International Journal of Data Warehousing and Mining 15, no. 3 (2019): 46–62. http://dx.doi.org/10.4018/ijdwm.2019070103.

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A data warehouse is considered a key aspect of success for any decision support system. Research on temporal databases have produced important results in this field, and data warehouses, which store historical data, can clearly benefit from such studies. A slowly changing dimension is a dimension in which any of its attributes in a data warehouse can change infrequently over time. Although different solutions have been proposed, each has its own particular disadvantages. The authors propose the Object-Relational Temporal Data Warehouse (O-RTDW) model for the slowly changing dimensions in this

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17

Hamoud, Alaa, Ali Hashim, and Wid Awadh. "CLINICAL DATA WAREHOUSE: A REVIEW." Iraqi Journal for Computers and Informatics 44, no. 2 (2018): 16–26. http://dx.doi.org/10.25195/ijci.v44i2.53.

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Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinic

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18

Aller, Raymond D. "The Clinical Laboratory Data Warehouse." American Journal of Clinical Pathology 120, no. 6 (2003): 817–19. http://dx.doi.org/10.1309/txxabu8mw75l04kf.

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19

Reddy, G. Sekhar, and Chittineni Suneetha. "UML-Based Data Warehouse Design Using Temporal Dimensional Modelling." International Journal of Security and Privacy in Pervasive Computing 12, no. 3 (2020): 1–19. http://dx.doi.org/10.4018/ijsppc.2020070101.

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The design of a data warehouse system deals with tasks such as data source administration, ETL processing, multidimensional modelling, data mart specification, and end-user tool development. In the last decade, numerous techniques have been presented to cover all the aspects of DW. However, none of these techniques stated the recent necessities of DW like visualization, temporal dimensions, record keeping, and so on. To overcome these issues, this paper proposes a UML based DW with temporal dimensions. This framework designs time-dependent DW that allows end-users to store history of variation

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20

Mohammed, Razi O., and Samani A. Talab. "Clinical Data Warehouse Issues and Challenges." International Journal of u- and e-Service, Science and Technology 7, no. 5 (2014): 251–62. http://dx.doi.org/10.14257/ijunesst.2014.7.5.22.

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21

Kong, Guilan, and Zhichun Xiao. "Protecting privacy in a clinical data warehouse." Health Informatics Journal 21, no. 2 (2014): 93–106. http://dx.doi.org/10.1177/1460458213504204.

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22

Taylor, M. "Design of an Integrated Clinical Data Warehouse." Journal of the Association for Laboratory Automation 5, no. 3 (2000): 54–59. http://dx.doi.org/10.1016/s1535-5535(04)00075-9.

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23

Gosain, Anjana, and Kriti Saroha. "Handling Bitemporal Schema Versions in Multi-temporal Environment for Data Warehouse." Arabian Journal for Science and Engineering 44, no. 4 (2018): 3619–38. http://dx.doi.org/10.1007/s13369-018-3609-0.

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24

Schaaf, T., T. Wetzel, C. Hahn, T. Schrader, T. Tolxdorff, and S. Hanß. "Integration of Decentralized Clinical Data in a Data Warehouse." Methods of Information in Medicine 48, no. 05 (2009): 414–18. http://dx.doi.org/10.3414/me9240.

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Summary Objectives: In this paper we present a general concept and describe the difficulties for the integration of data from various clinical partners in one data warehouse using the Open European Nephrology Science Center (OpEN.SC) as an example. This includes a requirements analysis of the data integration process and also the design according to these requirements. Methods: This conceptual approach based on the Rational Unified Process (RUP) and paradigm of Service-Oriented Architecture (SOA). Results: Because we have to enhance the confidence of our partners in the OpEN.SC system and with

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25

Mohammed, AbubakerElrazi O., and Samani A. Talab. "Enhanced Extraction Clinical Data Technique to Improve Data Quality in Clinical Data Warehouse." International Journal of Database Theory and Application 8, no. 3 (2015): 333–42. http://dx.doi.org/10.14257/ijdta.2015.8.3.29.

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26

Greenhalgh, Paul, Helen M. King, Kevin Muldoon-Smith, and Josephine Ellis. "The New Distribution: Spatio-Temporal Analysis of Large Distribution Warehouse Premises in England and Wales." Urban Planning 6, no. 3 (2021): 399–414. http://dx.doi.org/10.17645/up.v6i3.4222.

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This research addresses the deficit of empirical investigation of changes in industrial and warehouse property markets in the UK. It uses business rates (rating list) data for England and Wales to reveal changes in the quantum and distribution of premises over the last decade. Spatio-temporal analysis using geographical information systems identifies where new industrial and warehouse premises have been developed and examines spatial changes in the distribution of premises between the two sectors. The research focuses on the development of new large distribution warehouses (LDWs) to investigat

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27

Pugliese, Gina, and Martin S. Favero. "A Clinical Data Warehouse for Hospital Infection Control." Infection Control & Hospital Epidemiology 25, no. 11 (2004): 940. http://dx.doi.org/10.1017/s0195941700080875.

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28

Onukwugha, Eberechukwu, Tsung-Ying Lee, Johnson Abree, et al. "Factors Associated with Treatment Among Older Adults Diagnosed with Chronic Lymphocytic Leukemia: An Analysis Using Medicare Claims Data." Blood 138, Supplement 1 (2021): 1968. http://dx.doi.org/10.1182/blood-2021-148727.

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Abstract INTRODUCTION: Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in adults in the US. Sixty-seven percent of patients diagnosed with CLL are age 65 years or older. While new agents and treatment combinations have been approved for CLL and treatment guidelines take into consideration age, frailty, and comorbidity status, limited information exists on current prescribing patterns or the demographic and clinical characteristics of individuals receiving them. The objectives of this study were to: 1) characterize CLL treatment patterns and timing of treatment; 2) identi

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29

Rudikova, L. V., and E. V. Zhavnerko. "ABOUT DATA MODELING SUBJECT DOMAINS PRACTICE-ORIENTED DIRECTION FOR UNIVERSAL SYSTEM OF STORAGE AND PROCESSING DATA." «System analysis and applied information science», no. 3 (November 2, 2017): 4–12. http://dx.doi.org/10.21122/2309-4923-2017-3-4-12.

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This article describes data modeling for practice-oriented subject domains they are basis of general data model for data warehouse creation. Describes short subject domains characteristic relationship to different types of any human activities at the current time. Offered appropriate data models, considered relationship between them as data processing and data warehouse creation, which can be built on information data storage technology and which has some characteristics as extensible complex subject domain, data integration, which get from any data sources, data time invariance with required

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30

Kwakye, Michael Mireku. "Conceptual Model and Design of Semantic Trajectory Data Warehouse." International Journal of Data Warehousing and Mining 16, no. 3 (2020): 108–31. http://dx.doi.org/10.4018/ijdwm.2020070106.

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The trajectory patterns of a moving object in a spatio-temporal domain offers varied information in terms of the management of the data generated from the movement. The query results of trajectory objects from the data warehouse are usually not enough to answer certain trend behaviours and meaningful inferences without the associated semantic information of the trajectory object or the geospatial environment within a specified purpose or context. This article formulates and designs a generic ontology modelling framework that serves as the background model platform for the design of a semantic

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31

Choi, Jin Wook, Yeoun Hwa Lee, Ki Joong Kim, et al. "The Analysis of Clinical Information by Building the Clinical Data Warehouse." Journal of Korean Society of Medical Informatics 7, no. 1 (2001): 1. http://dx.doi.org/10.4258/jksmi.2001.7.1.1.

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32

Shin, Soo-Yong, Woo Sung Kim, and Jae-Ho Lee. "Characteristics Desired in Clinical Data Warehouse for Biomedical Research." Healthcare Informatics Research 20, no. 2 (2014): 109. http://dx.doi.org/10.4258/hir.2014.20.2.109.

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33

Persoon, L., S. Nijsten, C. Overhof, et al. "BENEFIT OF A CLINICAL DATA WAREHOUSE FOR DATA-COLLECTION IN RADIOTHERAPY." Radiotherapy and Oncology 92 (August 2009): S153. http://dx.doi.org/10.1016/s0167-8140(12)72987-0.

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34

Pecoraro, Fabrizio, Daniela Luzi, and Fabrizio L. Ricci. "Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes." ACI Open 03, no. 01 (2019): e44-e62. http://dx.doi.org/10.1055/s-0039-1688936.

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Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiauto

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35

Wisnubhadra, Irya, Safiza Baharin, and Nanna Herman. "Open Spatiotemporal Data Warehouse for Agriculture Production Analytics." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 419–31. http://dx.doi.org/10.22266/ijies2020.1231.37.

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Business Intelligence (BI) technology with Extract, Transform, and Loading process, Data Warehouse, and OLAP have demonstrated the ability of information and knowledge generation for supporting decision making. In the last decade, the advancement of the Web 2.0 technology is improving the accessibility of web of data across the cloud. Linked Open Data, Linked Open Statistical Data, and Open Government Data is increasing massively, creating a more significant computer-recognizable data available for sharing. In agricultural production analytics, data resources with high availability and accessi

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36

Eschrich, Steven A., Jamie K. Teer, Phillip Reisman, et al. "Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience." JCO Clinical Cancer Informatics, no. 5 (June 2021): 561–69. http://dx.doi.org/10.1200/cci.20.00175.

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PURPOSE The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS Moffitt Ca

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37

Walters, Kellie M., Anna Jojic, Emily R. Pfaff, et al. "Supporting research, protecting data: one institution’s approach to clinical data warehouse governance." Journal of the American Medical Informatics Association 29, no. 4 (2021): 707–12. http://dx.doi.org/10.1093/jamia/ocab259.

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Abstract Institutions must decide how to manage the use of clinical data to support research while ensuring appropriate protections are in place. Questions about data use and sharing often go beyond what the Health Insurance Portability and Accountability Act of 1996 (HIPAA) considers. In this article, we describe our institution’s governance model and approach. Common questions we consider include (1) Is a request limited to the minimum data necessary to carry the research forward? (2) What plans are there for sharing data externally?, and (3) What impact will the proposed use of data have on

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38

Wang, Liangjiang, Aidong Zhang, and Murali Ramanathan. "BioStar models of clinical and genomic data for biomedical data warehouse design." International Journal of Bioinformatics Research and Applications 1, no. 1 (2005): 63. http://dx.doi.org/10.1504/ijbra.2005.006903.

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Mia, Md Raihan, Abu Sayed Md Latiful Hoque, Shahidul Islam Khan, and Sheikh Iqbal Ahamed. "A privacy-preserving National Clinical Data Warehouse: Architecture and analysis." Smart Health 23 (March 2022): 100238. http://dx.doi.org/10.1016/j.smhl.2021.100238.

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Wisniewski, Mary F., Piotr Kieszkowski, Brandon M. Zagorski, William E. Trick, Michael Sommers, and Robert A. Weinstein. "Development of a Clinical Data Warehouse for Hospital Infection Control." Journal of the American Medical Informatics Association 10, no. 5 (2003): 454–62. http://dx.doi.org/10.1197/jamia.m1299.

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Grant, Andrew, Andriy Moshyk, Hassan Diab, et al. "Integrating feedback from a clinical data warehouse into practice organisation." International Journal of Medical Informatics 75, no. 3-4 (2006): 232–39. http://dx.doi.org/10.1016/j.ijmedinf.2005.07.037.

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42

Ko, Soo Jeong, Sang Jun Park, and Dong-Jin Chang. "Experience of Converting Clinical Data Warehouse to Common Data Model and Additional Data Loading." Health Insurance Review & Assessment Service Research 1, no. 2 (2021): 179–95. http://dx.doi.org/10.52937/hira.21.1.2.179.

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Onyebuchi, Amaonwu, Ugochukwu O. Matthew, Jazuli S. Kazaure, et al. "Business Demand for a Cloud Enterprise Data Warehouse in Electronic Healthcare Computing." International Journal of Cloud Applications and Computing 12, no. 1 (2022): 1–22. http://dx.doi.org/10.4018/ijcac.297098.

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Cloud enterprise data warehousing is a top level strategic business and information technology (IT) investment initiative in any organization that is technologically inclined, profit driven and customer oriented. To build the data warehouse, data are obtained from numerous heterogenous data sources, transformed, cleansed and processed into an applicable data repositories for implementation across the healthcare organizational settings. The current paper constructed an enterprise cloud data warehouse for e-healthcare organization and connected the medical/clinical workforces through the enterpr

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de Mul, Marleen, Peter Alons, Peter van der Velde, Ilse Konings, Jan Bakker, and Jan Hazelzet. "Development of a clinical data warehouse from an intensive care clinical information system." Computer Methods and Programs in Biomedicine 105, no. 1 (2012): 22–30. http://dx.doi.org/10.1016/j.cmpb.2010.07.002.

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François, Sandy, Colin H. Adler, Nyla I. Flowers, et al. "26207 Data extraction accuracy in stage III melanoma from a clinical data warehouse." Journal of the American Academy of Dermatology 85, no. 3 (2021): AB90. http://dx.doi.org/10.1016/j.jaad.2021.06.381.

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Jefferys, Benjamin R., Iheanyi Nwankwo, Elias Neri, et al. "Navigating legal constraints in clinical data warehousing: a case study in personalized medicine." Interface Focus 3, no. 2 (2013): 20120088. http://dx.doi.org/10.1098/rsfs.2012.0088.

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Personalized medicine relies in part upon comprehensive data on patient treatment and outcomes, both for analysis leading to improved models that provide the basis for enhanced treatment, and for direct use in clinical decision-making. A data warehouse is an information technology for combining and standardizing multiple databases. Data warehousing of clinical data is constrained by many legal and ethical considerations, owing to the sensitive nature of the data being stored. We describe an unconstrained clinical data warehousing architecture, some of the legal constraints that have led us to

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Rahm, Erhard, Toralf Kirsten, and Jörg Lange. "The GeWare data warehouse platform for the analysis of molecular-biological and clinical data." Journal of Integrative Bioinformatics 4, no. 1 (2007): 1–11. http://dx.doi.org/10.1515/jib-2007-47.

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Abstract We introduce the GeWare data warehouse platform for the integrated analysis of clinical information, microarray data and annotations within large biomedical research studies. Clinical data is obtained from a commercial study management system while publicly available data is integrated using a mediator approach. The platform utilizes a generic approach to manage different types of annotations. We outline the overall architecture of the platform, its implementation as well as the main processing and analysis workflows.

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Boussadi, Abdelali, Thibaut Caruba, Eric Zapletal, Brigitte Sabatier, Pierre Durieux, and Patrice Degoulet. "A clinical data warehouse-based process for refining medication orders alerts." Journal of the American Medical Informatics Association 19, no. 5 (2012): 782–85. http://dx.doi.org/10.1136/amiajnl-2012-000850.

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Cui, Hongwei, Qu Zhang, Wenfu Wu, Haolei Zhang, Jiangtao Ji, and Hao Ma. "Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage." Agriculture 12, no. 11 (2022): 1883. http://dx.doi.org/10.3390/agriculture12111883.

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Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain st

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Grammatico-Guillon, Leslie, Kimberly Shea, S. Reza Jafarzadeh, et al. "Antibiotic Prescribing in Outpatient Children: A Cohort From a Clinical Data Warehouse." Clinical Pediatrics 58, no. 6 (2019): 681–90. http://dx.doi.org/10.1177/0009922819834278.

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Aim. To characterize antibiotic (ab) prescriptions in children. Methods. Evaluation of outpatient ab prescriptions in a 3-year cohort of children in primary care using a data warehouse (Massachusetts Health Disparities Repository) by comorbid conditions, demographics, and clinical indication. Results. A total of 15 208 children with nearly 120 000 outpatient visits were included. About one third had a comorbid condition (most commonly asthma). Among the 30 000 ab prescriptions, first-line penicillins and macrolides represented the most frequent ab (70%), followed by cephalosporins (16%). Comor

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