Статті в журналах: "Temporal clinical data warehouse"

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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 (January 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 (November 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 (February 2, 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, 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-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 (November 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 (May 20, 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, and 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 (December 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, 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 (June 15, 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 (May 20, 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, 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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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 evolution of data model and versioning of dimensional model. These models have their own strengths and limitations, but none fully satisfies the above-stated broad range of aspects, making it difficult to compare the proposed schemes with one another. This paper analyses the schemes that satisfy such challenging aspects faced by a data warehouse and proposes taxonomy for characterizing the existing models to temporal data management in data warehouse. The paper also discusses some open challenges.

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Degoulet, P. "The Virtuous Circles of Clinical Information Systems: a Modern Utopia." Yearbook of Medical Informatics 25, no. 01 (August 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 virtuous circles are considered: user satisfaction vs. system use, system use vs. cost efficiency, and system use vs quality of care. Results: In structural equation models (SEM), the positive bidirectional relationship between user satisfaction and use was only observed in the early HEGP CIS deployment phase (first four years) but disappeared in late post-adoption (≥8 years). From 2009 to 2013, financial efficiency of 20 AP-HP hospitals evaluated with stochastic frontier analysis (SFA) models diminished by 0.5% per year. The lower decrease of efficiency observed between the three hospitals equipped with a more mature CIS and the 17 other hospitals was of the same order of magnitude than the difference observed between pediatric and non-pediatric hospitals. Outcome quality benefits that would bring evidence to the system use vs. quality loop are unlikely to be obtained in a near future since they require integration with population-based outcome measures including mortality, morbidity, and quality of life that may not be easily available. Conclusion: Barriers to making the transformation of the utopian part of the CIS virtuous circles happen should be overcome to actually benefit the emerging eSociety.

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

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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 (February 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 concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

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Atay, Canan Eren, and Georgia Garani. "Maintaining Dimension's History in Data Warehouses Effectively." International Journal of Data Warehousing and Mining 15, no. 3 (July 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 research work. Using this approach, it is possible to keep track of the whole history of an object in a data warehouse efficiently. The proposed model has been implemented on a real data set and tested successfully. Several limitations implied in other solutions, such as redundancy, surrogate keys, incomplete historical data, and creation of additional tables are not present in our solution.

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Hamoud, Alaa, Ali Hashim, and Wid Awadh. "CLINICAL DATA WAREHOUSE: A REVIEW." Iraqi Journal for Computers and Informatics 44, no. 2 (December 31, 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 clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW.

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

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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 (July 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 variations for long term. Besides, it authorizes to visualize the business goals of organizations in the form of attribute tree via UML, which is designed after receiving user necessities and later reconciling with temporal variables. The implementation of proposed technique is detailed with university education database for quality improvement. The proposed technique is found to be useful in terms of temporal dimension, long-term record keeping, and easy to make decision goals through attribute trees.

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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 (October 31, 2014): 251–62. http://dx.doi.org/10.14257/ijunesst.2014.7.5.22.

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Kong, Guilan, and Zhichun Xiao. "Protecting privacy in a clinical data warehouse." Health Informatics Journal 21, no. 2 (October 9, 2014): 93–106. http://dx.doi.org/10.1177/1460458213504204.

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Taylor, M. "Design of an Integrated Clinical Data Warehouse." Journal of the Association for Laboratory Automation 5, no. 3 (July 1, 2000): 54–59. http://dx.doi.org/10.1016/s1535-5535(04)00075-9.

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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 (October 31, 2018): 3619–38. http://dx.doi.org/10.1007/s13369-018-3609-0.

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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 this the willingness of them to participate, important requirements are controllability, transparency and security for all partners. Reusable and fine-grained components were found to be necessary when working with diverse data sources. With SOA the requested reusability is implemented easily. Conclusions: A key step in the development of a data integration process within such a health information system like OpEN.SC is to analyze the requirements. And to show that this is not only a theoretical work, we present a design – developed with RUP and SOA – which fulfills these requirements.

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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 (June 30, 2015): 333–42. http://dx.doi.org/10.14257/ijdta.2015.8.3.29.

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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 (September 23, 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 investigate whether there is a new pattern of warehouse premises located in close proximity to junctions on the national highway network. Findings confirm the emergence of a dynamic distribution warehouse property market where “super sheds” have been developed in areas with high levels of multi-modal connectivity. The comprehensive spatio-temporal analysis of all industrial and warehouse premises in England and Wales reconfigures the previously recognised Midlands “Golden Triangle” of distribution warehouses to a “Golden Pointer” and reveals the emergence of a rival “Northern Dumbbell” of distribution warehouse premises in the North of England. Further analysis using isochrones confirms that 85% of the population of Great Britain is situated within four hours average heavy goods vehicle drive time of these two concentrations of super sheds and over 60% of all LDWs floorspace is within 30 minutes’ drive of intermodal rail freight interchanges.

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

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Onukwugha, Eberechukwu, Tsung-Ying Lee, Johnson Abree, Catherine Cooke, Summers Amanda, Keri Yang, Sizhu Liu, Boxiong Tang, and Yared Jean. "Factors Associated with Treatment Among Older Adults Diagnosed with Chronic Lymphocytic Leukemia: An Analysis Using Medicare Claims Data." Blood 138, Supplement 1 (November 5, 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) identify factors associated with the receipt of CLL treatment in the US Medicare population. METHODS: The study sample included Medicare beneficiaries diagnosed with CLL from 2017 to 2019 using the Chronic Conditions Data Warehouse. Medicare beneficiaries were identified using billing diagnosis codes. Patients who were not treated for CLL during a continuous 6-month period (i.e., baseline period), continuously enrolled in Medicare Parts A, B, and D at baseline with no evidence of enrollment in Medicare Advantage, and age 65 or older at the index date were included. The index date was defined as the date of the first claim with a CLL diagnosis code during the cohort identification period (7/1/2017-6/30/2019). Individuals were followed from the index date until loss of eligibility, death, or end of the study period on 12/31/2019, whichever occurred first. We characterized CLL treatments using National Comprehensive Cancer Network guidelines and grouped individuals based on the first treatment received. The CLL treatments included: rituximab monotherapy, ibrutinib monotherapy, bendamustine/rituximab (BR), obinutuzumab, and other treatments. We reported the proportion of individuals who received the first course of treatment (COT), the top-ranked regimens within the first COT, and the median time to starting the first COT. Time-to-initiation was calculated as the time (in days) from the index date until the first evidence of treatment receipt. We utilized EventFlow visual analytics software to characterize longitudinal patterns of CLL treatments received from the index date (Day 0) until the end of follow-up. EventFlow facilitates exploratory, visual analyses of temporal event sequences using patient-level information about single events (e.g., infusion date), interval events (prescription start and end date), and the end-of-study indicator. We characterized the sample using the following baseline measures: age, race/ethnicity, gender, Charlson Comorbidity Index score, and use of preventive health services, among other measures. We identified factors associated with receipt of CLL treatment using a logistic regression model and reported the covariate-adjusted odds ratio (AOR). An AOR<1 indicates the comparison was negatively associated with receipt of CLL treatment. RESULTS: After applying the inclusion/exclusion criteria, 3,440 CLL patients were identified. Sixteen percent (n=556) of individuals received CLL treatment and the median follow up time was 540 days. Overall, the mean (standard error) age was 77 (8); 49% male. Among the 556 treated patients, the distribution of first COT was 35% ibrutinib, 34% rituximab, 12% BR, 4% obinutuzumab, and 14% other treatment. The median (interquartile range, mean) time to receipt of CLL treatment was 61 (224, 166) days. The median time to receipt of ibrutinib, rituximab, BR, or obinutuzumab was 109, 49, 53, and 140 days, respectively. The EventFlow graphic (Figure 1) illustrates treatments received (prescriptions or chemotherapy administrations) post-index date and through the end of the study period. Less than half of the patients in the BR group completed six doses of BR. Compared to patients in the rituximab group, a larger proportion of patients in the ibrutinib and BR groups remained on the first COT over time. In the logistic regression model age (≥85 vs 65-74; AOR=0.69; 95% CI:0.53-0.91) and gender (male vs female; AOR=1.28; 95% CI: 1.06 - 1.54) were statically significant; no other statistically significant differences based on baseline measures were observed. CONCLUSIONS: Among Medicare beneficiaries diagnosed with CLL, less than 2 out of 10 patients received CLL treatment. The most common treatments administered during this time period were ibrutinib or rituximab. Younger age and male gender were factors associated with increased receipt of treatment. Figure 1 Figure 1. Disclosures Onukwugha: BeiGene, Ltd.: Research Funding; Sanofi Aventis: Other: Faculty mentor on a pre-doctoral fellowship. Cooke: BeiGene, Ltd.: Research Funding. Yang: BeiGene, Ltd.: Current Employment. Liu: BeiGene, Ltd.: Current Employment. Tang: BeiGene, Ltd.: Current Employment.

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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 temporal marks, relatively high data stability, search necessary compromises in data redundancy, system blocks modularity, flexibility and extensibility of architecture, high requirements to data storage security. It’s proposed general approach of data collection and data storage, appropriate data models, in the future, will integrate in one database scheme and create generalized scheme of data warehouse as type «constellation of facts». For getting of data models applies structural methodology and consider general principles of conceptual design. Using complex system, which can work with some information sources and represent data in convenient view for users will in-demand for analysis data selected subject domains and determination of possible relationships.

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Kwakye, Michael Mireku. "Conceptual Model and Design of Semantic Trajectory Data Warehouse." International Journal of Data Warehousing and Mining 16, no. 3 (July 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 data warehouse for trajectories. The methodology underpins on higher granularity of data as a result of pre-processed and extract-transformed-load (ETL) data so as to offer efficient semantic inference to the underlying trajectory data. Moreover, the modelling approach outlines the thematic dimensions that offer a design platform for predictive trend analysis and knowledge discovery in the trajectory dynamics and data processing for moving objects.

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Choi, Jin Wook, Yeoun Hwa Lee, Ki Joong Kim, Joo Sung Kim, Joong Shin Park, Jung Han Song, Eun Joo Lee, Sun Goo Kim, Jong Deuk Kim, and Suhng Gwon Kim. "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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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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Persoon, L., S. Nijsten, C. Overhof, R. Debougnoux-Huppertz, D. De Ruysscher, P. Lambin, and A. Dekker. "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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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 (January 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 semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse. Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach. Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse.

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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 (December 31, 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 accessibility is a primary requirement. However, today’s data accessibility for production analytics is limited in the 2 or 3-stars open data format and rarely has attributes for spatiotemporal analytics. The new data warehouse concept has a new approach to combine the openness of data resources with mobility or spatiotemporal data in nature. This new approach could help the decision-makers to use external data to make a crucial decision more intuitive and flexible. This paper proposed the development of a spatiotemporal data warehouse with an integration process using service-oriented architecture and open data sources. The data sources are originating from the Village and Rural Area Information System (SIDeKa) that capture the agricultural production transaction in a daily manner. This paper also describes the way to spatiotemporal analytics for agricultural production using a new spatiotemporal data warehouse approach. The experiment results, by executing six relevant spatiotemporal query samples on DW with fact table contains 324096 tuples with temporal integer/float for each tuple, 4495 tuples of field dimension with geographic data as polygons, 80 tuples of village dimension, dozens of tuples of the district, subdistrict, province dimensions. The DW time dimension contains 3653 tuples representing a date for ten years, proved that this new approach has a convenient, simple model, and expressive performance for supporting executive to make decisions on agriculture production analytics based on spatiotemporal data. This research also underlines the prospects for scaling and nurturing the spatiotemporal data warehouse initiative.

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Eschrich, Steven A., Jamie K. Teer, Phillip Reisman, Erin Siegel, Chandan Challa, Patricia Lewis, Katherine Fellows, 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 Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case–focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

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Walters, Kellie M., Anna Jojic, Emily R. Pfaff, Marie Rape, Donald C. Spencer, Nicholas J. Shaheen, Brent Lamm, and Timothy S. Carey. "Supporting research, protecting data: one institution’s approach to clinical data warehouse governance." Journal of the American Medical Informatics Association 29, no. 4 (December 6, 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 patients and the institution? In 2020, 302 of the 319 requests reviewed were approved. The majority of requests were approved in less than 2 weeks, with few or no stipulations. For the remaining requests, the governance committee works with researchers to find solutions to meet their needs while also addressing our collective goal of protecting patients.

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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 (September 2003): 454–62. http://dx.doi.org/10.1197/jamia.m1299.

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

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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 (November 30, 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, Nwamaka U. Okafor, Ogobuchi Daniel Okey, Prisca I. Okochi, Janet Folasade Taiwo, and Ani Okechukwu Matthew. "Business Demand for a Cloud Enterprise Data Warehouse in Electronic Healthcare Computing." International Journal of Cloud Applications and Computing 12, no. 1 (January 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 enterprise e-healthcare data warehouse and allowed the medical solutions and clinical information of all the patient to be stored. The proposed system is expected to improved the e-Healthcare information management by providing a model to support medical software automation, hardware system integration and enhances the control and management of the patients records.

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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 (January 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, Kendra B. Little, Michael C. Lowe, Suephy C. Chen, and Howa Yeung. "26207 Data extraction accuracy in stage III melanoma from a clinical data warehouse." Journal of the American Academy of Dermatology 85, no. 3 (September 2021): AB90. http://dx.doi.org/10.1016/j.jaad.2021.06.381.

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Jefferys, Benjamin R., Iheanyi Nwankwo, Elias Neri, David C. W. Chang, Lev Shamardin, Stefanie Hänold, Norbert Graf, Nikolaus Forgó, and Peter Coveney. "Navigating legal constraints in clinical data warehousing: a case study in personalized medicine." Interface Focus 3, no. 2 (April 6, 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 reconsider this architecture, and the legal and technical solutions to these constraints developed for the clinical data warehouse in the personalized medicine project p-medicine. We also propose some changes to the legal constraints that will further enable clinical research.

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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 (March 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 (September 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 (November 9, 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 storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the XOY, XOZ and YOZ planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (XOY, XOZ and YOZ planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (R2), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3-setting method, with SSE, R2 and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing.

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Grammatico-Guillon, Leslie, Kimberly Shea, S. Reza Jafarzadeh, Ingrid Camelo, Zoha Maakaroun-Vermesse, Marisol Figueira, William G. Adams, and Steve Pelton. "Antibiotic Prescribing in Outpatient Children: A Cohort From a Clinical Data Warehouse." Clinical Pediatrics 58, no. 6 (March 19, 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%). Comorbid children had 54.3 ab prescriptions/100 child-years versus 38.8 in children without comorbidity; ab prescription was higher in urinary tract infections (>60% of episodes), otitis, lower respiratory tract infections (>50%), especially in comorbid children and children under 2 year old. Ab prescriptions were significantly associated with younger age, emergency room visit, comorbid children, and acute infections. Discussion. A clinical data warehouse could help in designing appropriate antimicrobial stewardship programs and represent a potential assessment tool.

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