Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A method for interoperable knowledge-based data quality assessment

ABSTRACT:

Background

Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results.

Objectives

To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations.

Methods

We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool—openCQA—that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models.

Results

Applying the method on the study’s dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment.

Conclusions

The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01458-1.

SUBMITTER: Tute E

PROVIDER: S-EPMC7942002 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

TBDQ: A Pragmatic Task-Based Method to Data Quality Assessment and Improvement.

Project description:Organizations are increasingly accepting data quality (DQ) as a major key to their success. In order to assess and improve DQ, methods have been devised. Many of these methods attempt to raise DQ by directly manipulating low quality data. Such methods operate reactively and are suitable for organizations with highly developed integrated systems. However, there is a lack of a proactive DQ method for businesses with weak IT infrastructure where data quality is largely affected by tasks that are performed by human agents. This study aims to develop and evaluate a new method for structured data, which is simple and practical so that it can easily be applied to real world situations. The new method detects the potentially risky tasks within a process, and adds new improving tasks to counter them. To achieve continuous improvement, an award system is also developed to help with the better selection of the proposed improving tasks. The task-based DQ method (TBDQ) is most appropriate for small and medium organizations, and simplicity in implementation is one of its most prominent features. TBDQ is case studied in an international trade company. The case study shows that TBDQ is effective in selecting optimal activities for DQ improvement in terms of cost and improvement.

| S-EPMC4871700 | biostudies-literature

An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0.

Project description:Cohort identification for clinical studies tends to be laborious, time-consuming, and expensive. Developing automated or semi-automated methods for cohort identification is one of the "holy grails" in the field of biomedical informatics. We propose a high-throughput similarity-based cohort identification algorithm by applying numerical abstractions on Electronic Health Records (EHR) data. We implement this algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which enables sites using this standardized EHR data representation to avail this algorithm with minimum effort for local implementation. We validate its performance for a retrospective cohort identification task on six clinical trials conducted at the Columbia University Medical Center. Our algorithm achieves an average Area Under the Curve (AUC) of 0.966 and an average Precision at 5 of 0.983. This interoperable method promises to achieve efficient cohort identification in EHR databases. We discuss suitable applications of our method and its limitations and propose warranted future work.

| S-EPMC5536903 | biostudies-literature

INTEGRATING TRANSCRIPTOMICS INTO TRIAD-BASED SOIL QUALITY ASSESSMENT

Project description:This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history traitM-bM-^@M-^Ys stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress. We used a one-color microarray design where each sample was hybridized to a single array

2014-02-01 | E-GEOD-52393 | biostudies-arrayexpress

INTEGRATING TRANSCRIPTOMICS INTO TRIAD-BASED SOIL QUALITY ASSESSMENT

Project description:This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history trait’s stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress.

2014-02-01 | GSE52393 | GEO

CCR+: Metadata Based Extended Personal Health Record Data Model Interoperable with the ASTM CCR Standard.

Project description:ObjectivesExtension of the standard model while retaining compliance with it is a challenging issue because there is currently no method for semantically or syntactically verifying an extended data model. A metadata-based extended model, named CCR+, was designed and implemented to achieve interoperability between standard and extended models.MethodsFurthermore, a multilayered validation method was devised to validate the standard and extended models. The American Society for Testing and Materials (ASTM) Community Care Record (CCR) standard was selected to evaluate the CCR+ model; two CCR and one CCR+ XML files were evaluated.ResultsIn total, 188 metadata were extracted from the ASTM CCR standard; these metadata are semantically interconnected and registered in the metadata registry. An extended-data-model-specific validation file was generated from these metadata. This file can be used in a smartphone application (Health Avatar CCR+) as a part of a multilayered validation. The new CCR+ model was successfully evaluated via a patient-centric exchange scenario involving multiple hospitals, with the results supporting both syntactic and semantic interoperability between the standard CCR and extended, CCR+, model.ConclusionsA feasible method for delivering an extended model that complies with the standard model is presented herein. There is a great need to extend static standard models such as the ASTM CCR in various domains: the methods presented here represent an important reference for achieving interoperability between standard and extended models.

| S-EPMC3950264 | biostudies-literature

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning.

Project description:Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.

| S-EPMC9219089 | biostudies-literature

Interoperable and accessible census and survey data from IPUMS.

Project description:The first version of the Integrated Public Use Microdata Series (IPUMS) was released to users in 1993, and since that time IPUMS has come to stand for interoperable and accessible census and survey data. Initially created to harmonize U.S. census microdata over time, IPUMS now includes microdata from the U.S. and international censuses and from surveys on health, employment, and other topics. IPUMS also provides geo-spatial data, aggregate population data, and environmental data. IPUMS supports ten data products, each disseminating an integrated data collection with a set of tools that make complex data easy to find, access, and use. Key features are record-level integration to create interoperable datasets, user-friendly interfaces, and comprehensive metadata and documentation. The IPUMS philosophy aligns closely with the FAIR principles of findability, accessibility, interoperability, and re-usability. IPUMS data have catalyzed knowledge generation across a wide range of social science and other disciplines, as evidenced by the large volume of publications and other products created by the vast IPUMS user community.

| S-EPMC5827695 | biostudies-literature

Knowledge-Based Biomedical Data Science.

Project description:Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.

| S-EPMC8095730 | biostudies-literature

Water quality assessment based on the water quality index method in Lake Poyang: The largest freshwater lake in China.

Project description:Twenty-four samplings were conducted every 3 months at 15 sites from January 2009 to October 2014 in Lake Poyang, and 20 parameters were analyzed and classified into three groups (toxic metals, easily treated parameters, and others). The assessment results based on water quality index (WQI) showed that the water quality in Lake Poyang was generally "moderate", according to the classification of the surface water quality standard (GB3838-2002) in China, but a deteriorating trend was observed at the interannual scale. Seasonally, the water quality was best in summer and worst in winter. Easily treated parameters generally determined the WQI value in the assessment, especially total nitrogen (TN) and total phosphorus (TP), while toxic metals and other parameters in Lake Poyang were generally at low and safe levels for drinking water. Water level (WL) has a net positive effect on water quality in Lake Poyang through dilution of environmental parameters, which in practice means TN. Consequently, local management agencies should pay more attention to nutrient concentrations during the monitoring schedule, as well as during the low-water periods which manifest a relatively bad water quality state, especially with the prevailing low WL observed recently in Lake Poyang.

| S-EPMC5740168 | biostudies-literature

New Quality-Range-Setting Method Based on Between- and Within-Batch Variability for Biosimilarity Assessment.

Project description:Analytical biosimilarity assessment relies on two implicit conditions. First, the analytical method must meet a set of requirements known as fit for intended use related to trueness and precision. Second, the manufacture of the reference drug product must be under statistical quality control; i.e., the between-batch variability is not larger than the expected within-batch variability. In addition, the quality range (QR) method is based on one sample per batch to avoid biased standard deviations in unbalanced studies. This, together with the small number of reference drug product batches, leads to highly variable QR bounds. In this paper, we propose to set the QR bounds from variance components estimated using a two-level nested linear model, accounting for between- and within-batch variances of the reference drug product. In this way, the standard deviation used to set QR is equal to the square root of the sum of between-batch variance plus the within-batch variance estimated by the maximum likelihood method. The process of this method, which we call QRML, is as follows. First, the condition of statistical quality control of the manufacture process is tested. Second, confidence intervals for QR bounds lead to an analysis of the reliability of the biosimilarity assessment. Third, after analyzing the molecular weight and dimer content of seven batches of a commercial bevacizumab drug product, we concluded that the QRML method was more reliable than QR.

| S-EPMC8226621 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data