scholarly journals Transformation of microbiology data into a standardised data representation using OpenEHR

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Antje Wulff ◽  
◽  
Claas Baier ◽  
Sarah Ballout ◽  
Erik Tute ◽  
...  
Keyword(s):  
Real Life ◽  
Primary Source ◽  
Multidrug Resistant ◽  
Data Representation ◽  
Data Models ◽  
Outbreak Detection ◽  
Standard Data ◽  
Quality Checks ◽  
Computer Based ◽  
Control Application

AbstractThe spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.

scholarly journals Potential of standard perinatal data for measuring maternal integrity in childbirth

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
C Miani ◽  
S Batram-Zantvoort ◽  
O Razum
Keyword(s):  
Real Life ◽  
Healthcare Setting ◽  
Demographic Variables ◽  
Care Providers ◽  
Birth Process ◽  
Standard Data ◽  
Obstetric Violence ◽  
Relevant Variables ◽  
Perinatal Data ◽  
Birth Centres

Abstract Background Measuring the phenomenon of violation of maternal integrity in childbirth (e.g. obstetric violence) relies in part on the completeness of maternity care providers' data. The population coverage and linkage possibilities that they provide make for a great untapped potential. Although violation of integrity is a complex phenomenon best measured with dedicated instruments, standard data provide details about the birth and care received. Relevant variables include justifications of medical procedures (e.g. episiotomy) and characteristics of the birth process (e.g. length of labour). Demographic variables can be used for intersectional analyses to track potential discrimination -a dimension of violation of integrity in childbirth. Methods Using a baseline questionnaire and perinatal data obtained from hospitals, birth centres and midwifes in the BaBi study (Germany), we compared the completeness of integrity-relevant variables across providers and depending on the demographic and clinical characteristics of the women. We investigated potential for analysis from an intersectional perspective. Results Our analyses included 908 births, of which 32 outside hospital. There were 634 vaginal birth vs. 274 caesarean sections. We found poor reporting on demographic variables, in particular with regard to the 'region of origin' variable (correct origin recorded for half of the migrants). There was better reporting by midwives than by hospitals for “soft indicators”, such as the position of the women during birth (100% vs. 87.6%). Conclusions Putting more emphasis on completeness of standardised data could increase their potential for research. Healthcare setting, organisational culture and working conditions might determine what is judged important in terms of reporting; therefore, targeted education may improve this process. Next, we will interview care providers to understand data collection constraints and priorities and potential reporting bias in real-life settings.

A Study of XML Models for Data Mining

Data Mining ◽  
2013 ◽  
pp. 1-27
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

Databases Modeling of Engineering Information

2009 ◽  
pp. 338-361
Author(s):  
Z. M. Ma
Keyword(s):  
Data Storage ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Database Modeling ◽  
Business Applications ◽  
Database Models ◽  
Engineering Information ◽  
Computer Based ◽  
Conceptual Data

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

scholarly journals Indefinite Proximity Learning: A Review

2015 ◽  
Vol 27 (10) ◽  
pp. 2039-2096 ◽  
Author(s):  
Frank-Michael Schleif ◽  
Peter Tino
Keyword(s):  
Large Scale ◽  
Data Representation ◽  
Easy Access ◽  
Proximity Data ◽  
Standard Data ◽  
Comprehensive Survey ◽  
Proximity Measures ◽  
The Individual ◽  
Efficient Learning ◽  
One Class Classification

Efficient learning of a data analysis task strongly depends on the data representation. Most methods rely on (symmetric) similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel or branch-and-bound approaches. Similarities and dissimilarities are, however, often naturally obtained by nonmetric proximity measures that cannot easily be handled by classical learning algorithms. Major efforts have been undertaken to provide approaches that can either directly be used for such data or to make standard methods available for these types of data. We provide a comprehensive survey for the field of learning with nonmetric proximities. First, we introduce the formalism used in nonmetric spaces and motivate specific treatments for nonmetric proximity data. Second, we provide a systematization of the various approaches. For each category of approaches, we provide a comparative discussion of the individual algorithms and address complexity issues and generalization properties. In a summarizing section, we provide a larger experimental study for the majority of the algorithms on standard data sets. We also address the problem of large-scale proximity learning, which is often overlooked in this context and of major importance to make the method relevant in practice. The algorithms we discuss are in general applicable for proximity-based clustering, one-class classification, classification, regression, and embedding approaches. In the experimental part, we focus on classification tasks.

scholarly journals The Plan-a-Day Approach to Measuring Planning Ability in Patients with Schizophrenia

2011 ◽  
Vol 17 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Daniel V. Holt ◽  
Katlehn Rodewald ◽  
Mirjam Rentrop ◽  
Joachim Funke ◽  
Matthias Weisbrod ◽  
...  
Keyword(s):  
Neuropsychological Tests ◽  
Real Life ◽  
Incremental Validity ◽  
Global Assessment Of Functioning ◽  
Activity Schedules ◽  
Everyday Functioning ◽  
Neuropsychological Measures ◽  
Planning Ability ◽  
Ability Tests ◽  
Computer Based

AbstractDeficits in executive functioning are closely related to the level of everyday functioning in patients with schizophrenia. However, many existing neuropsychological measures are limited in their ability to predict functional outcome. To contribute towards closing this gap, we developed a computer-based test of planning ability (“Plan-a-Day”) that requires participants to create daily activity schedules in a simulated work setting. Eighty patients diagnosed with schizophrenia were tested with Plan-a-Day and a battery of cognitive ability tests. Plan-a-Day showed satisfactory psychometric properties in terms of consistency, reliability, and construct validity. Compared to other neuropsychological tests used in this study, it also demonstrated incremental validity with regard to the Global Assessment of Functioning. The Plan-a-Day approach, therefore, seems to represent a valid alternative for measuring planning ability in patients with executive function deficits, occupying a middle ground between traditional neuropsychological tests and real-life assessments. (JINS, 2011,17, 327–335)

Public health impacts of culture independent diagnostic testing in Australia

2017 ◽  
Vol 38 (4) ◽  
pp. 162
Author(s):  
Fiona J May
Keyword(s):  
Public Health ◽  
Health Management ◽  
Diagnostic Testing ◽  
Primary Source ◽  
Outbreak Detection ◽  
Surveillance Systems ◽  
Advantages And Disadvantages ◽  
Testing Protocol ◽  
Culture Independent ◽  
The Impact

Culture independent diagnostic tests (CIDT) for detection of pathogens in clinical specimens have become widely adopted in Australian pathology laboratories. Pathology laboratories are the primary source of notification of pathogens to state and territory surveillance systems. Monitoring and analysis of surveillance data is integral to guiding public health actions to reduce the incidence of disease and respond to outbreaks. As with any change in testing protocol, the advantages and disadvantages of the change from culture based testing to culture independent testing need to be weighed up and the impact on surveillance and outbreak detection assessed. This article discusses the effect of this change in testing on surveillance and public health management of pathogens in Australia, with specific focus on gastrointestinal pathogens.

Graph Visualization Styles for Use in Configuration Management: A User Study

2015 ◽  
Author(s):  
Keith Phelan ◽  
Crystal Wilson ◽  
Joshua D. Summers ◽  
Mary E. Kurz
Keyword(s):  
Change Management ◽  
User Study ◽  
Data Representation ◽  
The Other ◽  
Visualization Tool ◽  
Graph Visualization ◽  
Response Accuracy ◽  
Configuration Change ◽  
Computer Based ◽  
Different Levels

The purpose of this research is to conduct a user study in order to determine the effect of numerous variables for data representation on the ability to answer questions about the system being represented. This research will be used in the development of a computer-based visualization tool to support configuration change management. The researchers hypothesized that the graph geometry and order of the questions being asked would not affect the results, while the color of the graph and the information being represented would affect the number of correct responses. The results showed an increase in the response accuracy for the answerable questions when the amount of information displayed in the data representation was minimized. On the other hand, none of the other factors showed to have a significant effect on the accuracy of the responses. The most significant limitation in this study was the possibility for different users putting different levels of effort into answering the questions.

Cognitive Interventions: Brain Training and Rehabilitation

2018 ◽  
Author(s):  
Thomas Swirsky-Sacchetti ◽  
Robert L. Rider
Keyword(s):  
Beneficial Effect ◽  
Cognitive Skills ◽  
Real Life ◽  
Cognitive Remediation ◽  
Future Research ◽  
Brain Training ◽  
Cognitive Domains ◽  
Cognitive Interventions ◽  
The Face ◽  
Computer Based

The research pertaining to two types of cognitive interventions is reviewed. Brain training, which utilizes a variety of computer based approaches, is designed to improve normal performance by developing cognitive skills. Cognitive remediation is designed to improve performance in the face of acquired deficits. Such programs are designed to help patients with existing mild cognitive impairment or dementia. Although not uniform, there is mounting evidence that these interventions have a beneficial effect. However, research also suggests the beneficial effect of such programs is often limited to the specific type of task trained, with generalization to real-life performance more questionable. Patients improve specific cognitive domains that do not necessarily correlate with improvements in activities of daily life. The findings and limitations of current research are discussed along with directions for future research.

scholarly journals Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs

2020 ◽  
Vol 41 (9) ◽  
pp. 1016-1021
Author(s):  
Meghan A. Baker ◽  
Deborah S. Yokoe ◽  
John Stelling ◽  
Ken Kleinman ◽  
Rebecca E. Kaganov ◽  
...  
Keyword(s):  
Infection Prevention ◽  
Detection System ◽  
Statistical Significance ◽  
Prevention Programs ◽  
Multidrug Resistant ◽  
Outbreak Detection ◽  
Convenience Sample ◽  
Clostridioides Difficile ◽  
Surveillance Programs ◽  
Infection Preventionists

AbstractObjective:To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.Design:Multicenter retrospective cohort study.Setting:The study included 43 hospitals using a common infection prevention surveillance system.Methods:A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.Results:We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.Conclusions:Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

Late Night in the Lion's Den: Chinese Restaurant-Nightclubs in 1940s San Francisco

2004 ◽  
Vol 4 (4) ◽  
pp. 94-101
Author(s):  
harley spiller
Keyword(s):  
New York ◽  
Social History ◽  
San Francisco ◽  
Real Life ◽  
Primary Source ◽  
Chinese American ◽  
Late Night ◽  
Chinese Restaurant ◽  
Food And Drink ◽  
The U.S

Late Night in the Lion's Den: A Social History of Chinese-American Restaurant-Nightclubs in the 1940s Restaurant menus can serve as excellent primary source material for social histories. The springboard for this article is a 1940s menu from San Francisco's Lion's Den, a Chinese-American restaurant and nightclub. A thorough review of the food and drink offerings is bolstered by an interview with a former Lion's Den dancer and emcee, Ms. Nora Wong. She tells stories of growing up Chinese in the U.S., and provides vivid insight into the real life of Chinese performers in the mid-twentieth century. The article is illustrated with period menus and the first-ever public glimpse of a telling behind-the-scene photograph from Ms. Wong's personal album. Beginning with 1930s Shanghai, the world nightlife capital that inspired imitators in the U.S., this article explores the naissance, development, heated competition, and eventual demise of Chinese nightclubs in both California and New York City. Many Chinese restaurant/nightclubs of the period are discussed, and other well-known performers are featured. Other topics discussed include the Western exotification of Asia, stereotyping, sexism, and racism.

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