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@article{1863062,
author = {Haneef, Romana and Tijhuis, Mariken and Thiebaut, Rodolphe and Májek, Ondřej and Pristas, Ivan and Tolenan, Hanna and Gallay, Anne},
article_location = {LONDON},
article_number = {1},
doi = {http://dx.doi.org/10.1186/s13690-021-00770-6},
keywords = {Data linkage; Linked data; Machine learning techniques; Artificial intelligence; Guidelines; Methodological guidelines; Statistical techniques; Population health research; Health indicators},
language = {eng},
issn = {0778-7367},
journal = {ARCHIVES OF PUBLIC HEALTH},
title = {Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques},
url = {https://archpublichealth.biomedcentral.com/articles/10.1186/s13690-021-00770-6},
volume = {80},
year = {2022}
}
TY - JOUR
ID - 1863062
AU - Haneef, Romana - Tijhuis, Mariken - Thiebaut, Rodolphe - Májek, Ondřej - Pristas, Ivan - Tolenan, Hanna - Gallay, Anne
PY - 2022
TI - Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques
JF - ARCHIVES OF PUBLIC HEALTH
VL - 80
IS - 1
SP - 1-12
EP - 1-12
PB - BIOMED CENTRAL LTD
SN - 07787367
KW - Data linkage
KW - Linked data
KW - Machine learning techniques
KW - Artificial intelligence
KW - Guidelines
KW - Methodological guidelines
KW - Statistical techniques
KW - Population health research
KW - Health indicators
UR - https://archpublichealth.biomedcentral.com/articles/10.1186/s13690-021-00770-6
N2 - 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.
ER -
HANEEF, Romana, Mariken TIJHUIS, Rodolphe THIEBAUT, Ondřej MÁJEK, Ivan PRISTAS, Hanna TOLENAN and Anne GALLAY. Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques. \textit{ARCHIVES OF PUBLIC HEALTH}. LONDON: BIOMED CENTRAL LTD, 2022, vol.~80, No~1, p.~1-12. ISSN~0778-7367. Available from: https://dx.doi.org/10.1186/s13690-021-00770-6.
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