Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022
dc.contributor.author | Badiola Zabala, Goizalde | |
dc.contributor.author | López Guede, José Manuel | |
dc.contributor.author | Estévez Sanz, Julián | |
dc.contributor.author | Graña Romay, Manuel María | |
dc.date.accessioned | 2024-04-08T16:13:29Z | |
dc.date.available | 2024-04-08T16:13:29Z | |
dc.date.issued | 2024-03-07 | |
dc.identifier.citation | Electronics 13(6) : (2024) // Article ID 1005 | es_ES |
dc.identifier.issn | 2079-9292 | |
dc.identifier.uri | http://hdl.handle.net/10810/66551 | |
dc.description.abstract | Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation. | es_ES |
dc.description.sponsorship | The first author received research funds from the Basque Government as the head of the Grupo de Inteligencia Computacional, Universidad del Pais Vasco, UPV/EHU, from 2007 until 2025. The current code for the grant is IT1689-22. Additionally, the author participates in Elkartek project KK-2022/00051. The Spanish MCIN has also granted the authors a research project under code PID2020-116346GB-I00. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2020-116346GB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | |
dc.subject | COVID-19 | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | artificial intelligence | es_ES |
dc.subject | mortality | es_ES |
dc.subject | prediction | es_ES |
dc.subject | risk factors | es_ES |
dc.subject | drug repurposing | es_ES |
dc.subject | drug | es_ES |
dc.title | Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022 | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2024-03-27T13:16:14Z | |
dc.rights.holder | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2079-9292/13/6/1005 | es_ES |
dc.identifier.doi | 10.3390/electronics13061005 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala |
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Except where otherwise noted, this item's license is described as © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).