Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis

Chen, Ruiyao; Chen, Jiayuan; Luo, Shuqing; Xiao, Zhongzhou; Lu, Lu; Liang, Bing; Liu, Sichen; Shi, Hao; Xu, Jianpeng

doi:10.1016/j.ijmedinf.2023.105151

Cited by 2 publications

(1 citation statement)

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“…ML algorithms are extensively researched in the diagnosis and prognosis of COVID-19. In a meta-analysis of 33 studies, the average performance of ML models in mortality prediction was 0.93 for AUC [ 19 ]. In 2,924 patients with 152 features, including age, gender, comorbid diseases, vital signs at baseline, clinical symptoms, and laboratory tests, the GB model showed the best result with an AUC of 0.94 [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

Panç,

Hürsoy,

Başaran

et al. 2023

Cureus

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BackgroundThe COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. MethodologyIn this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. ResultsWhen the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. ConclusionsThe study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.

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Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

Panç,

Hürsoy,

Başaran

et al. 2023

Cureus

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COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm

Hamar,

Mohammed,

Váradi

et al. 2024

Sci Rep

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The emergence of newer SARS-CoV-2 variants of concern (VOCs) profoundly changed the ICU demography; this shift in the virus’s genotype and its correlation to lethality in the ICUs is still not fully investigated. We aimed to survey ICU patients’ clinical and laboratory parameters in correlation with SARS-CoV-2 variant genotypes to lethality. 503 COVID-19 ICU patients were included in our study beginning in January 2021 through November 2022 in Hungary. Furthermore, we implemented random forest (RF) as a potential predictor regarding SARS-CoV-2 lethality among 649 ICU patients in two ICU centers. Survival analysis and comparison of hypertension (HT), diabetes mellitus (DM), and vaccination effects were conducted. Logistic regression identified DM as a significant mortality risk factor (OR: 1.55, 95% CI 1.06–2.29, p = 0.025), while HT showed marginal significance. Additionally, vaccination demonstrated protection against mortality (p = 0.028). RF detected lethality with 81.42% accuracy (95% CI 73.01–88.11%, [AUC]: 91.6%), key predictors being PaO2/FiO2 ratio, lymphocyte count, and chest Computed Tomography Severity Score (CTSS). Although a smaller number of patients require ICU treatment among Omicron cases, the likelihood of survival has not proportionately increased for those who are admitted to the ICU. In conclusion, our RF model supports more effective clinical decision-making among ICU COVID-19 patients.

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Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis

Cited by 2 publications

References 53 publications

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm

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