Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European Countries

Background The COVID-19 pandemic has led many individuals to miss essential care. Machine-learning models that predict which patients are at greatest risk of missing care visits can help health administrators prioritize retentions efforts towards patients with the most need. Such approaches may be e...

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Main Authors:	Reuter, Anna (Author) , Smolić, Šime (Author) , Bärnighausen, Till (Author) , Sudharsanan, Nikkil (Author)
Format:	Article (Journal) Chapter/Article
Language:	English
Published:	March 04, 2022
Edition:	Preprint
In:	medRxiv Year: 2022, Pages: 1-21
DOI:	10.1101/2022.03.01.22271611
Online Access:	Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1101/2022.03.01.22271611 Verlag, lizenzpflichtig, Volltext: http://medrxiv.org/lookup/doi/10.1101/2022.03.01.22271611
Author Notes:	Anna Reuter, Prof. Šime Smolić, Prof. Dr. Till Bärnighausen, Prof. Dr. Nikkil Sudharsanan

Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European countries by Reuter, Anna (Author) , Smolić, Šime (Author) , Bärnighausen, Till (Author) , Sudharsanan, Nikkil (Author) ,

in: BMC health services research, 23 (2023), Artikel-ID 544, Seite 1-12

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Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European Countries

Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European countries by Reuter, Anna (Author) , Smolić, Šime (Author) , Bärnighausen, Till (Author) , Sudharsanan, Nikkil (Author) ,

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