Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning.
Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase,...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 4; pp. 957 - 969 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155874089&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155874089 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2022 vid: 60 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155874089 155874089 NLM35147843 10.1007/s11517-022-02519-x NLM35147843 155874089 ppf: 957 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning. aug: au: Saadatmand, Sara Salimifard, Khodakaram Mohammadi, Reza Marzban, Maryam Naghibzadeh-Tahami, Ahmad affil: Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, 75169, Bushehr, Iran sug: ab: Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient's background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient's background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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