research-article

Predicting Criticality in COVID-19 Patients

Authors:

Roger A. Hallman,

Anjali Chikkula,

Temiloluwa PrioleauAuthors Info & Claims

BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Article No.: 46, Pages 1 - 7

https://doi.org/10.1145/3388440.3412463

Published: 10 November 2020 Publication History

Abstract

The COVID-19 pandemic has infected millions of people around the world, spreading rapidly and causing a flood of patients that risk overwhelming clinical facilities. Whether in urban or rural areas, hospitals have limited resources and personnel to treat critical infections in intensive care units, which must be allocated effectively. To assist clinical staff in deciding which patients are in the greatest need of critical care, we develop a predictive model based on a publicly-available data set that is rich in clinical markers. We perform statistical analysis to determine which clinical markers strongly correlate with hospital admission, semi-intensive care, and intensive care for COVID-19 patients. We create a predictive model that will assist clinical personnel in determining COVID-19 patient prognosis. Additionally, we take a step towards a global framework for COVID-19 prognosis prediction by incorporating statistical data for geographically and ethnically diverse COVID--19 patient sets into our own model. To the best of our knowledge, this is the first model which does not exclusively utilize local data.

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cover image ACM Conferences

BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 2020

193 pages

ISBN:9781450379649

DOI:10.1145/3388440

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 10 November 2020

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BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 21 - 24, 2020

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Cited By

Stojanović DJovanović DStojanović N(2024)Big Medical Data Analytics Using Apache Spark Framework2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)10.1109/IcETRAN62308.2024.10645083(1-5)Online publication date: 3-Jun-2024
https://doi.org/10.1109/IcETRAN62308.2024.10645083
Abbasi Habashi SKoyuncu MAlizadehsani R(2023)A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence TechniquesDiagnostics10.3390/diagnostics1310174913:10(1749)Online publication date: 16-May-2023
https://doi.org/10.3390/diagnostics13101749
Famiglini LCampagner ACarobene ACabitza F(2022)A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patientsMedical & Biological Engineering & Computing10.1007/s11517-022-02543-xOnline publication date: 30-Mar-2022
https://doi.org/10.1007/s11517-022-02543-x
Famiglini LBini GCarobene ACampagner ACabitza F(2021)Prediction of ICU admission for COVID-19 patients: a Machine Learning approach based on Complete Blood Count data2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS52027.2021.00065(160-165)Online publication date: Jun-2021
https://doi.org/10.1109/CBMS52027.2021.00065
AlJame MImtiaz AAhmad IMohammed A(2021)Deep forest model for diagnosing COVID-19 from routine blood testsScientific Reports10.1038/s41598-021-95957-w11:1Online publication date: 17-Aug-2021
https://doi.org/10.1038/s41598-021-95957-w

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