Pregled bibliografske jedinice broj: 1213190
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 // medRxiv: the preprint server for health sciences (2022) doi:10.1101/2022.03.01.22271611 (znanstveni, poslan)
CROSBI ID: 1213190 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Predicting missed health care visits during the
COVID-19 pandemic using machine learning methods:
Evidence from 55,500 individuals from 28 European
Countries
Autori
Reuter, Anna ; Smolić, Šime ; Bärnighausen, Till ; Sudharsanan, Nikkil
Vrsta, podvrsta
Radovi u časopisima,
znanstveni
Izvornik
MedRxiv: the preprint server for health sciences (2022)
Status rada
Poslan
Ključne riječi
machine learning algorithms ; Covid-19 ; missed health care visits ; SHARE Corona Survey ;
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Javno zdravstvo i zdravstvena zaštita, Ekonomija, Interdisciplinarne društvene znanosti
POVEZANOST RADA
Projekti:
EK-ESF-UP.01.3.2.03.0001 - SHARE - Istraživanje o zdravlju, starenju i umirovljenju u Europi (SHARE) (Smolić, Šime; Čipin, Ivan, EK - UP.01.3.2.03) ( CroRIS)
EK-H2020-101015924 - Neplanirani zdravstveni, ekonomski i socijalni učinci odluka o kontroli epidemije COVID-19: spoznaje iz SHARE-a (SHARE-COVID19) (Smolić, Šime, EK - H2020-SC1-PHE-CORONAVIRUS-2020-2-RTD) ( CroRIS)
Ustanove:
Ekonomski fakultet, Zagreb
Profili:
Šime Smolić
(autor)
