Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
See more >
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Reduce
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Language
EN
Article de revue
This item was published in
Archives of Public Health. 2022-01-04, vol. 80, n° 1, p. 9
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Artificial intelligence
Data linkage
Guidelines
Health indicators
Linked data
Machine learning techniques
Methodological guidelines
Population health research
Statistical techniques