Your browser doesn't support javascript.
Can machine learning models predict maternal and newborn healthcare providers' perception of safety during the COVID-19 pandemic? A cross-sectional study of a global online survey.
Hammoud, Bassel; Semaan, Aline; Elhajj, Imad; Benova, Lenka.
  • Hammoud B; Biomedical Engineering Program, Faculty of Medicine-Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon. basseladelhammoud@gmail.com.
  • Semaan A; Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.
  • Elhajj I; Electrical and Computer Engineering Department, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
  • Benova L; Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.
Hum Resour Health ; 20(1): 63, 2022 08 19.
Article in English | MEDLINE | ID: covidwho-2002194
ABSTRACT

BACKGROUND:

Maternal and newborn healthcare providers are essential professional groups vulnerable to physical and psychological risks associated with the COVID-19 pandemic. This study uses machine learning algorithms to create a predictive tool for maternal and newborn healthcare providers' perception of being safe in the workplace globally during the pandemic.

METHODS:

We used data collected between 24 March and 5 July 2020 through a global online survey of maternal and newborn healthcare providers. The questionnaire was available in 12 languages. To predict healthcare providers' perception of safety in the workplace, we used features collected in the questionnaire, in addition to publicly available national economic and COVID-19-related factors. We built, trained and tested five machine learning models Support Vector Machine (SVM), Random Forest (RF), XGBoost, CatBoost and Artificial Neural Network (ANN) for classification and regression. We extracted from RF models the relative contribution of features in output prediction.

RESULTS:

Models included data from 941 maternal and newborn healthcare providers from 89 countries. ML models performed well in classification and regression tasks, whereby RF had 82% cross-validated accuracy for classification, and CatBoost with 0.46 cross-validated root mean square error for regression. In both classification and regression, the most important features contributing to output prediction were classified as three themes (1) information accessibility, clarity and quality; (2) availability of support and means of protection; and (3) COVID-19 epidemiology.

CONCLUSION:

This study identified salient features contributing to maternal and newborn healthcare providers perception of safety in the workplace. The developed tool can be used by health systems globally to allow real-time learning from data collected during a health system shock. By responding in real-time to the needs of healthcare providers, health systems could prevent potential negative consequences on the quality of care offered to women and newborns.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Infant, Newborn Language: English Journal: Hum Resour Health Year: 2022 Document Type: Article Affiliation country: S12960-022-00758-5

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Infant, Newborn Language: English Journal: Hum Resour Health Year: 2022 Document Type: Article Affiliation country: S12960-022-00758-5