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5 results on '"Alaa, Ahmed M."'

1. Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.

Author

Baqui P, Marra V, Alaa AM, Bica I, Ercole A, and van der Schaar M

Subjects
Brazil epidemiology, Brazil ethnology, COVID-19 ethnology, COVID-19 therapy, Female, Hospitalization, Humans, Male, Socioeconomic Factors, COVID-19 mortality, Hospital Mortality, Machine Learning, Models, Biological, Pandemics, SARS-CoV-2
Abstract

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810-0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization., (© 2021. The Author(s).)

Published
2021
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4. CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19

Author

Qian, Zhaozhi, Alaa, Ahmed M, Van Der Schaar, Mihaela, Qian, Zhaozhi [0000-0002-4561-0342], and Apollo - University of Cambridge Repository

Subjects
FOS: Computer and information sciences, Automated machine learning, Computer Science - Machine Learning, Resource planning, Statistics - Machine Learning, Healthcare, COVID-19, Gaussian processes, Machine Learning (stat.ML), Compartmental models, ComputingMilieux_MISCELLANEOUS, Machine Learning (cs.LG)
Abstract

Funder: University of Cambridge, The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)-a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic-we conclude the paper with a summary of the lessons learned from this experience.

Published
2020

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