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1.
PLoS One ; 16(12): e0261594, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34919569

RESUMO

Client-Server (C/S) application is always used in the existing Human Resource Management System (HRMS) as the system architecture, which has the problems of complex maintenance and poor compatibility; and cannot use professional database and development system, making the system development difficult and the data security low. To solve the above problems, the overall demand is analyzed, as well as feasibility and key technologies of the enterprise HRMS system. Then a HRMS is designed and developed, based on the user's key functional requirements and related technologies, which is reasonable and easy to maintain. The system is supported by Browser-Server (B/S) structure, with the current popular Java 2 Platform Enterprise Edition (J2EE) multi-level structure as the overall architecture. The mature Microsoft SQL Server 2008 introduced by Microsoft is used as the database platform. Combined with Model View Controller (MVC) design pattern, this system can be used by users without geographical restrictions and system maintenance. In this system, performance logic and business logic are separated, which makes it convenient for the development and maintenance of the system. The system mainly includes six modules: personnel management, organizational management, recruitment management, training management, salary management and system management, which integrates enterprise information and realizes the functions of easy access and easy query of information database. Its interface is simple, easy to understand, and easy to operate, with low investment, low cost, high safety, good performance and easy maintenance, which help to improve the work efficiency and modern management level of enterprises. In the end, the operation performance of the system is tested. The results show that the throughput of the main functional modules in the system is greater than 100 times/s when dealing with the business, and the success rate of event processing is greater than 99%. The average response time of the business end is less than 0.4 s, and the average response time of the terminal side is less than 0.5 s, which all meet the standards. System CPU occupancy rate can be basically controlled below 30%, and memory usage rate is below 30%. In summary, the system designed here has the basic functions but also to ensure good performance, suitable for enterprise personnel management, organizational management, recruitment management, training management and salary management. The design and development of this system aims to provide technical support for the service quality of enterprise human resource management business, to improve the overall efficiency, promote the pace of enterprise strategic development, and enhance the market competitiveness of enterprises.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Bases de Dados Factuais , Recursos Humanos , Computadores , Humanos , Software , Interface Usuário-Computador
2.
Geospat Health ; 16(2)2021 10 19.
Artigo em Inglês | MEDLINE | ID: mdl-34672181

RESUMO

Medicinal plants are increasingly used, both for medical applications and personal healthcare. However, existing herbal database systems for plant retrieval offer only basic information and do not support real-time analysis of the spatial aspects of plantations and distribution sites. Moreover, data records are usually static and not publicly available as they rely on costly proprietary software packages. To address these shortcomings, including limiting the time needed for collection and data processing, a novel medicinal plants geospatial database management system is proposed. The system allows localization of plant sites and data presentation on an interactive heat map displaying spatial information of plants selected by the user within a specific radius from the user's location, including automatic presentation of an itinerary giving the optimal route between user location and plant destinations selected. The approach relies on dynamic and role-based data management, an interactive map that includes graphics and integrated geospatial analyses thanks to cross-platform, geographical a JavaScript library and Google API. Both spatial data and attributes are available in real time. The system would support effective collaboration, among herb farmers, government agencies, private investors, healthcare professionals and the general public with regard to various aspects of medicinal plants and their applications.


Assuntos
Plantas Medicinais , Sistemas de Gerenciamento de Base de Dados , Bases de Dados Factuais
3.
Multimedia | Recursos Multimídia | ID: multimedia-9239

RESUMO

Capacitação sobre o Levantamento Rápido de Índices para Aedes Aegypti (LIRAa) Responsáveis: Gerência de Vigilância das Arboviroses Gerência de Vigilância e Controle das Doenças Tropicais Negligenciadas Diretoria de Vigilância das Doenças Vetoriais e Zoonoses (DVDVZ) Superintendência de Vigilância em Saúde (SVS) Secretaria de Estado da Saúde do Tocantins (SES-TO)


Assuntos
Entomologia/estatística & dados numéricos , Sistemas de Gerenciamento de Base de Dados/instrumentação
4.
PLoS Comput Biol ; 17(8): e1009283, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34379637

RESUMO

Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and to lead to new insights. However, developing bespoke analysis pipelines from scratch is time-consuming, and general tools for exploring such heterogeneous data are not available. We argue that by treating all data as text, a knowledge-base can accommodate a range of bioinformatic data types and applications. We show that a database coupled to nearest-neighbor algorithms can address common tasks such as gene-set analysis as well as specific tasks such as ontology translation. We further show that a mathematical transformation motivated by diffusion can be effective for exploration across heterogeneous datasets. Diffusion enables the knowledge-base to begin with a sparse query, impute more features, and find matches that would otherwise remain hidden. This can be used, for example, to map multi-modal queries consisting of gene symbols and phenotypes to descriptions of diseases. Diffusion also enables user-driven learning: when the knowledge-base cannot provide satisfactory search results in the first instance, users can improve the results in real-time by adding domain-specific knowledge. User-driven learning has implications for data management, integration, and curation.


Assuntos
Bases de Conhecimento , Aprendizagem , Integração de Sistemas , Interface Usuário-Computador , Algoritmos , Sistemas de Gerenciamento de Base de Dados , Humanos
6.
PLoS One ; 16(8): e0255562, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34411131

RESUMO

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others' efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.


Assuntos
Algoritmos , Computação em Nuvem/estatística & dados numéricos , Gerenciamento de Dados/métodos , Sistemas de Gerenciamento de Base de Dados/normas , Bases de Dados Factuais , Armazenamento e Recuperação da Informação/estatística & dados numéricos , Software
7.
Radiol Med ; 126(10): 1296-1311, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34213702

RESUMO

Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.


Assuntos
Aprendizado Profundo , Diagnóstico por Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Medicina de Precisão/métodos , Fluxo de Trabalho , Algoritmos , Consenso , Análise de Dados , Mineração de Dados/métodos , Sistemas de Gerenciamento de Base de Dados/organização & administração , Diagnóstico por Imagem/estatística & dados numéricos , Genômica/métodos , Humanos , Aprendizado de Máquina , Oncologia , Neoplasias/diagnóstico por imagem , Redes Neurais de Computação , Neuroimagem , Prognóstico , Sistemas de Informação em Radiologia
9.
PLoS One ; 16(5): e0250992, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33974672

RESUMO

With the rapid advancement of information and communication technologies, there is a growing transformation of healthcare systems. A patient's health data can now be centrally stored in the cloud and be shared with multiple healthcare stakeholders, enabling the patient to be collaboratively treated by more than one healthcare institution. However, several issues, including data security and privacy concerns still remain unresolved. Ciphertext-policy attribute-based encryption (CP-ABE) has shown promising potential in providing data security and privacy in cloud-based systems. Nevertheless, the conventional CP-ABE scheme is inadequate for direct adoption in a collaborative ehealth system. For one, its expressiveness is limited as it is based on a monotonic access structure. Second, it lacks an attribute/user revocation mechanism. Third, the computational burden on both the data owner and data users is linear with the number of attributes in the ciphertext. To address these inadequacies, we propose CESCR, a CP-ABE for efficient and secure sharing of health data in collaborative ehealth systems with immediate and efficient attribute/user revocation. The CESCR scheme is unbounded, i.e., it does not bind the size of the attribute universe to the security parameter, it is based on the expressive and non-restrictive ordered binary decision diagram (OBDD) access structure, and it securely outsources the computationally demanding attribute operations of both encryption and decryption processes without requiring a dummy attribute. Security analysis shows that the CESCR scheme is secure in the selective model. Simulation and performance comparisons with related schemes also demonstrate that the CESCR scheme is expressive and efficient.


Assuntos
Segurança Computacional , Prestação Integrada de Cuidados de Saúde/tendências , Registros Eletrônicos de Saúde , Disseminação de Informação , Telemedicina , Simulação por Computador , Sistemas de Gerenciamento de Base de Dados , Prestação Integrada de Cuidados de Saúde/métodos , Humanos , Telemedicina/métodos
10.
PLoS One ; 16(5): e0251483, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34003830

RESUMO

The purposes are to manage human resource data better and explore the association between Human Resource Management (HRM), data mining, and economic management. An Ensemble Classifier-Decision Tree (EC-DT) algorithm is proposed based on the single decision tree algorithm to analyze HRM data. The involved single decision tree algorithms include C4.5, Random Tree, J48, and SimpleCart. Then, an HRM system is established based on the designed algorithm, and the evaluation management and talent recommendation modules are tested. Finally, the designed algorithm is compared and tested. Experimental results suggest that C4.5 provides the highest classification accuracy among the single decision tree algorithms, reaching 76.69%; in contrast, the designed EC-DT algorithm can provide a classification accuracy of 79.97%. The proposed EC-DT algorithm is compared with the Content-based Recommendation Method (CRM) and the Collaborative Filtering Recommendation Method (CFRM), revealing that its Data Mining Recommendation Method (DMRM) can provide the highest accuracy and recall, reaching 35.2% and 41.6%, respectively. Therefore, the data mining-based HRM system can promote and guide enterprises to develop according to quantitative evaluation results. The above results can provide a reference for studying HRM systems based on data mining technology.


Assuntos
Mineração de Dados/métodos , Recursos Humanos , Algoritmos , Sistemas de Gerenciamento de Base de Dados , Árvores de Decisões , Humanos
11.
J Korean Med Sci ; 36(19): e134, 2021 May 17.
Artigo em Inglês | MEDLINE | ID: mdl-34002552

RESUMO

During the three the coronavirus disease 2019 (COVID-19) surges in South Korea, there was a shortage of hospital beds for COVID-19 patients, and as a result, there were cases of death while waiting for hospitalization. To minimize the risk of death and to allow those confirmed with COVID-19 to safely wait for hospitalization at home, the local government of Gyeonggi-do in South Korea developed a novel home management system (HMS). The HMS team, comprised of doctors and nurses, was organized to operate HMS. HMS provided a two-way channel for the taskforce and patients to monitor the severity of patient's condition and to provide healthcare counseling as needed. In addition, the HMS team cooperated with a triage/bed assignment team to expedite the response in case of an emergency, and managed a database of severity for real-time monitoring of patients. The HMS became operational for the first time in August 2020, initially managing only 181 patients; it currently manages a total of 3,707 patients. The HMS supplemented the government's COVID-19 confirmed case management framework by managing patients waiting at home for hospitalization due to lack of hospital and residential treatment center beds. HMS also could contribute a sense of psychological stability in patients and prevented the situation from worsening by efficient management of hospital beds and reduction of workloads on public healthcare centers. To stabilize and improve the management of COVID-19 confirmed cases, governments should organically develop self-treatment and HMS, and implement a decisive division of roles within the local governments.


Assuntos
COVID-19/terapia , Serviços de Assistência Domiciliar/organização & administração , Assistência Domiciliar/organização & administração , Governo Local , Pandemias , SARS-CoV-2 , COVID-19/epidemiologia , Aconselhamento , Sistemas de Gerenciamento de Base de Dados , Bases de Dados Factuais , Necessidades e Demandas de Serviços de Saúde , Serviços de Assistência Domiciliar/estatística & dados numéricos , Assistência Domiciliar/estatística & dados numéricos , Número de Leitos em Hospital , Humanos , Equipe de Assistência ao Paciente , República da Coreia/epidemiologia , Autocuidado , Listas de Espera
12.
Database (Oxford) ; 20212021 05 18.
Artigo em Inglês | MEDLINE | ID: mdl-34003247

RESUMO

Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Bases de Dados Factuais
13.
Mil Med ; 186(9-10): 1001-1009, 2021 08 28.
Artigo em Inglês | MEDLINE | ID: mdl-33591328

RESUMO

INTRODUCTION: Tracking measures of quality over time has been shown to improve care within institutions and across health systems. Perioperative quality assurance (QA) tracking by anesthesia departments in the Military Health System (MHS) has not used a uniform system integrated into the workflow of anesthesia providers. The purpose of this study was to demonstrate that the use of the embedded QA outcome reporting feature in the anesthesia information management system (AIMS) increased the rate of reporting compared to the current paper reporting system in a military anesthesia department. MATERIALS AND METHODS: An electronic outcome reporting mechanism embedded in the AIMS was activated as an alternative to paper QA outcome reporting. The proportion of anesthesia cases per month in a 12-month period with a reported QA outcome was compared to the previous year in which only the paper reporting system was used. The total number of cases in each time period with an outcome reported was compared using chi square for proportions, and systems were evaluated using the Statistical Process Control methodology. This project was evaluated and determined to be exempt from review by our institutional review board. RESULTS: There was a 389.8% increase in the number of cases with a QA outcome reported after the implementation of the outcome reporting function integrated into the AIMS (χ2 = 207.72; P <.001, Table I). Systems before and after the intervention were stable, and special cause variation was noted only at the point of implementation of the electronic reporting system. Anesthesia providers were surveyed and felt that the addition of QA reporting to the AIMS made QA reporting more likely. CONCLUSIONS: The use of an electronic QA outcome reporting method integrated into the AIMS dramatically increased the likelihood that a QA outcome would be reported. The decreased administrative burden of the integrated outcome reporting system was likely the primary reason for this increase. This study was limited by the fact that it was done in a single institution; however, the size and timing of the increase clearly indicate that the intervention was the reason for improved reporting. Electronic health record upgrades should consider incorporating QA reporting into the AIMS across the MHS. These measures could allow for system-wide improvement, evaluation, and evidence-based education on their own, but also by facilitating participation in the American Society of Anesthesiologists' Anesthesia Quality Institute's National Anesthesia Clinical Outcomes Registry. This report serves as a valuable example to institutions and perioperative leaders in the MHS of how to improve the robustness of perioperative QA reporting such that it could be used to validate and improve the value of care.


Assuntos
Anestesia , Anestesiologia , Sistemas de Gerenciamento de Base de Dados , Humanos , Gestão da Informação , Garantia da Qualidade dos Cuidados de Saúde , Fluxo de Trabalho
14.
Database (Oxford) ; 20212021 02 06.
Artigo em Inglês | MEDLINE | ID: mdl-33547799

RESUMO

Molecular causal interactions are defined as regulatory connections between biological components. They are commonly retrieved from biological experiments and can be used for connecting biological molecules together to enable the building of regulatory computational models that represent biological systems. However, including a molecular causal interaction in a model requires assessing its relevance to that model, based on the detailed knowledge about the biomolecules, interaction type and biological context. In order to standardize the representation of this knowledge in 'causal statements', we recently developed the Minimum Information about a Molecular Interaction Causal Statement (MI2CAST) guidelines. Here, we introduce causalBuilder: an intuitive web-based curation interface for the annotation of molecular causal interactions that comply with the MI2CAST standard. The causalBuilder prototype essentially embeds the MI2CAST curation guidelines in its interface and makes its rules easy to follow by a curator. In addition, causalBuilder serves as an original application of the Visual Syntax Method general-purpose curation technology and provides both curators and tool developers with an interface that can be fully configured to allow focusing on selected MI2CAST concepts to annotate. After the information is entered, the causalBuilder prototype produces genuine causal statements that can be exported in different formats.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Humanos , Anotação de Sequência Molecular
15.
BMC Bioinformatics ; 22(1): 1, 2021 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-33388027

RESUMO

BACKGROUND: Protein-peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein-peptide databases and tools that allow the retrieval, characterization and understanding of protein-peptide recognition and consequently support peptide design. RESULTS: We introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein-peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein-peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation. CONCLUSIONS: Propedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein-peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Bases de Dados de Proteínas , Peptídeos/química , Proteínas/química , Algoritmos , Humanos
16.
Med Care ; 59: S42-S50, 2021 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-33438882

RESUMO

OBJECTIVE: To examine sex differences in risk for administratively documented suicide attempt (SA) among US Army soldiers during the Iraq/Afghanistan wars. METHOD: Using administrative person-month records of Regular Army enlisted soldiers from 2004 to 2009, we identified 9650 person-months with a first documented SA and an equal-probability control sample (n=153,528 person-months). Person-months were weighted to the population and pooled over time. After examining the association of sex with SA in a logistic regression analysis, predictors were examined separately among women and men. RESULTS: Women (an estimated 13.7% of the population) accounted for 25.2% of SAs and were more likely than men to attempt suicide after adjusting for sociodemographic, service-related, and mental health diagnosis (MHDx) variables (odds ratio=1.6; 95% confidence interval, 1.5-1.7). Women with increased odds of SA in a given person-month were younger, non-Hispanic White, less educated, in their first term of enlistment, never or previously deployed (vs. currently deployed), and previously received a MHDx. The same variables predicted SA among men. Interactions indicated significant but generally small differences between women and men on 6 of the 8 predictors, the most pronounced being time in service, deployment status, and MHDx. Discrete-time survival models examining risk by time in service demonstrated that patterns for women and men were similar, and that women's initially higher risk diminished as time in service increased. CONCLUSIONS: Predictors of documented SAs are similar for US Army women and men. Differences associated with time in service, deployment status, and MHDx require additional research. Future research should consider stressors that disproportionately affect women.


Assuntos
Militares/estatística & dados numéricos , Tentativa de Suicídio/estatística & dados numéricos , Adulto , Campanha Afegã de 2001- , Estudos de Coortes , Sistemas de Gerenciamento de Base de Dados , Feminino , Humanos , Guerra do Iraque 2003-2011 , Estudos Longitudinais , Masculino , Estudos Retrospectivos , Fatores Sexuais , Estados Unidos , United States Department of Defense , Adulto Jovem
17.
J Chem Inf Model ; 61(2): 554-559, 2021 02 22.
Artigo em Inglês | MEDLINE | ID: mdl-33502186

RESUMO

Presently, quantum chemical calculations are widely used to generate extensive data sets for machine learning applications; however, generally, these sets only include information on equilibrium structures and some close conformers. Exploration of potential energy surfaces provides important information on ground and transition states, but analysis of such data is complicated due to the number of possible reaction pathways. Here, we present RePathDB, a database system for managing 3D structural data for both ground and transition states resulting from quantum chemical calculations. Our tool allows one to store, assemble, and analyze reaction pathway data. It combines relational database CGR DB for handling compounds and reactions as molecular graphs with a graph database architecture for pathway analysis by graph algorithms. Original condensed graph of reaction technology is used to store any chemical reaction as a single graph.


Assuntos
Algoritmos , Sistemas de Gerenciamento de Base de Dados , Bases de Dados Factuais
18.
Anesth Analg ; 132(2): 465-474, 2021 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-32332291

RESUMO

BACKGROUND: Many hospitals have replaced their legacy anesthesia information management system with an enterprise-wide electronic health record system. Integrating the anesthesia data within the context of the global hospital information infrastructure has created substantive challenges for many organizations. A process to build a perioperative data warehouse from Epic was recently published from the University of California Los Angeles (UCLA), but the generalizability of that process is unknown. We describe the implementation of their process at the University of Miami (UM). METHODS: The UCLA process was tested at UM, and performance was evaluated following the configuration of a reporting server and transfer of the required Clarity tables to that server. Modifications required for the code to execute correctly in the UM environment were identified and implemented, including the addition of locally specified elements in the database. RESULTS: The UCLA code to build the base tables in the perioperative data warehouse executed correctly after minor modifications to match the local server and database architecture at UM. The 26 stored procedures in the UCLA process all ran correctly using the default settings provided and populated the base tables. After modification of the item lists to reflect the UM implementation of Epic (eg, medications, laboratory tests, physiologic monitors, and anesthesia machine parameters), the UCLA code ran correctly and populated the base tables. The data from those tables were used successfully to populate the existing perioperative data warehouse at UM, which housed data from the legacy anesthesia information management system of the institution. The time to pull data from Epic and populate the perioperative data warehouse was 197 ± 47 minutes (standard deviation [SD]) on weekdays and 260 ± 56 minutes (SD) on weekend days, measured over 100 consecutive days. The longer times on weekends reflect the simultaneous execution of database maintenance tasks on the reporting server. The UCLA extract process has been in production at UM for the past 18 months and has been invaluable for quality assurance, business process, and research activities. CONCLUSIONS: The data schema developed at UCLA proved to be a practical and scalable method to extract information from the Epic electronic health system database into the perioperative data warehouse in use at UM. Implementing the process developed at UCLA to build a comprehensive perioperative data warehouse from Epic is an extensible process that other hospitals seeking more efficient access to their electronic health record data should consider.


Assuntos
Data Warehousing , Sistemas de Gerenciamento de Base de Dados , Registros Eletrônicos de Saúde , Sistemas de Informação Hospitalar , Acesso à Informação , Mineração de Dados , Bases de Dados Factuais , Humanos , Assistência Perioperatória
19.
Brief Bioinform ; 22(1): 474-484, 2021 01 18.
Artigo em Inglês | MEDLINE | ID: mdl-31885044

RESUMO

BACKGROUND: With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS: We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION: BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.


Assuntos
Biologia Computacional/métodos , Sistemas de Gerenciamento de Base de Dados , Gerenciamento de Dados/métodos , Bases de Dados de Compostos Químicos , Bases de Dados Genéticas , Humanos
20.
Methods Mol Biol ; 2199: 191-207, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33125652

RESUMO

iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .


Assuntos
Sistemas de Gerenciamento de Base de Dados , Bases de Dados de Proteínas , Internet , Mapeamento de Interação de Proteínas , Interface Usuário-Computador , Humanos
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