J 2022

Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques

HANEEF, Romana, Mariken TIJHUIS, Rodolphe THIEBAUT, Ondřej MÁJEK, Ivan PRISTAS et. al.

Basic information

Original name

Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques

Authors

HANEEF, Romana (guarantor), Mariken TIJHUIS, Rodolphe THIEBAUT, Ondřej MÁJEK (203 Czech Republic, belonging to the institution), Ivan PRISTAS, Hanna TOLENAN and Anne GALLAY

Edition

ARCHIVES OF PUBLIC HEALTH, LONDON, BIOMED CENTRAL LTD, 2022, 0778-7367

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30304 Public and environmental health

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 3.300

RIV identification code

RIV/00216224:14110/22:00126141

Organization unit

Faculty of Medicine

DOI

UT WoS

000738623200002

Keywords in English

Data linkage; Linked data; Machine learning techniques; Artificial intelligence; Guidelines; Methodological guidelines; Statistical techniques; Population health research; Health indicators

Tags

Tags

International impact, Reviewed
Změněno: 28/6/2022 08:31, Mgr. Tereza Miškechová

Abstract

V originále

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.