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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 (CROSBI ID 328035)

Prilog u časopisu | ostalo

Reuter, Anna ; Smolić, Šime ; Bärnighausen, Till ; Sudharsanan, Nikkil Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: Evidence from 55,500 individuals from 28 European Countries medRxiv: the preprint server for health sciences, (2022), doi: 10.1101/2022.03.01.22271611

Podaci o odgovornosti

Reuter, Anna ; Smolić, Šime ; Bärnighausen, Till ; Sudharsanan, Nikkil

engleski

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

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 especially useful for efficiently targeting interventions for health systems overburdened by the COVID-19 pandemic.

machine learning algorithms ; Covid-19 ; missed health care visits ; SHARE Corona Survey ;

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Podaci o izdanju

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2022.

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1468-5833

10.1101/2022.03.01.22271611

Povezanost rada

Ekonomija, Interdisciplinarne društvene znanosti, Javno zdravstvo i zdravstvena zaštita

Poveznice