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Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques
R. Haneef, M. Tijhuis, R. Thiébaut, O. Májek, I. Pristaš, H. Tolonen, A. Gallay
Language English Country Great Britain
Document type Journal Article
Grant support
801553 / InfAct
European Commission
NLK
BioMedCentral
from 2009-12-01
BioMedCentral Open Access
from 2011
Directory of Open Access Journals
from 2011
Free Medical Journals
from 2009
PubMed Central
from 2009
Europe PubMed Central
from 2009
ProQuest Central
from 2009-01-01
Open Access Digital Library
from 2009-01-01
Open Access Digital Library
from 2011-01-01
Public Health Database (ProQuest)
from 2009-01-01
ROAD: Directory of Open Access Scholarly Resources
from 1990
Springer Nature OA/Free Journals
from 2009-12-01
- Publication type
- Journal Article MeSH
BACKGROUND: The capacity to use data linkage and artificial intelligence to estimate and predict health indicators varies across European countries. However, the estimation of health indicators from linked administrative data is challenging due to several reasons such as variability in data sources and data collection methods resulting in reduced interoperability at various levels and timeliness, availability of a large number of variables, lack of skills and capacity to link and analyze big data. The main objective of this study is to develop the methodological guidelines calculating population-based health indicators to guide European countries using linked data and/or machine learning (ML) techniques with new methods. METHOD: We have performed the following step-wise approach systematically to develop the methodological guidelines: i. Scientific literature review, ii. Identification of inspiring examples from European countries, and iii. Developing the checklist of guidelines contents. RESULTS: We have developed the methodological guidelines, which provide a systematic approach for studies using linked data and/or ML-techniques to produce population-based health indicators. These guidelines include a detailed checklist of the following items: rationale and objective of the study (i.e., research question), study design, linked data sources, study population/sample size, study outcomes, data preparation, data analysis (i.e., statistical techniques, sensitivity analysis and potential issues during data analysis) and study limitations. CONCLUSIONS: This is the first study to develop the methodological guidelines for studies focused on population health using linked data and/or machine learning techniques. These guidelines would support researchers to adopt and develop a systematic approach for high-quality research methods. There is a need for high-quality research methodologies using more linked data and ML-techniques to develop a structured cross-disciplinary approach for improving the population health information and thereby the population health.
Bordeaux University Bordeaux School of Public Health Bordeaux France
Department of Non Communicable Diseases and Injuries Santé Publique France Saint Maurice France
Finnish Institute for Health and Welfare Helsinki Finland
INSERM INRIA SISTM team Bordeaux Population health Bordeaux France
Institute of Biostatistics and Analyses Faculty of Medicine Masaryk University Brno Czech Republic
Institute of Health Information and Statistics of the Czech Republic Prague Czech Republic
Medical Information Department Bordeaux University Hospital Bordeaux France
National Institute for Public Health and the Environment Bilthoven The Netherlands
National Institute of public health division of health informatics and biostatistics Zagreb Croatia
References provided by Crossref.org
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- $a BACKGROUND: The capacity to use data linkage and artificial intelligence to estimate and predict health indicators varies across European countries. However, the estimation of health indicators from linked administrative data is challenging due to several reasons such as variability in data sources and data collection methods resulting in reduced interoperability at various levels and timeliness, availability of a large number of variables, lack of skills and capacity to link and analyze big data. The main objective of this study is to develop the methodological guidelines calculating population-based health indicators to guide European countries using linked data and/or machine learning (ML) techniques with new methods. METHOD: We have performed the following step-wise approach systematically to develop the methodological guidelines: i. Scientific literature review, ii. Identification of inspiring examples from European countries, and iii. Developing the checklist of guidelines contents. RESULTS: We have developed the methodological guidelines, which provide a systematic approach for studies using linked data and/or ML-techniques to produce population-based health indicators. These guidelines include a detailed checklist of the following items: rationale and objective of the study (i.e., research question), study design, linked data sources, study population/sample size, study outcomes, data preparation, data analysis (i.e., statistical techniques, sensitivity analysis and potential issues during data analysis) and study limitations. CONCLUSIONS: This is the first study to develop the methodological guidelines for studies focused on population health using linked data and/or machine learning techniques. These guidelines would support researchers to adopt and develop a systematic approach for high-quality research methods. There is a need for high-quality research methodologies using more linked data and ML-techniques to develop a structured cross-disciplinary approach for improving the population health information and thereby the population health.
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