Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

Viderman, Dmitriy; Kotov, Alexander; Popov, Maxim; Abdildin, Yerkin

doi:10.1016/j.ijmedinf.2023.105308

Cited by 5 publications

(1 citation statement)

References 94 publications

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“…Among the machine learning algorithms we tested, the artificial neural network had the highest AUC and the best calibration. This suggests that the artificial neural network can accurately discriminate between severe and non-severe pneumonia cases, and can provide reliable probability estimates of severe pneumonia ( 20 ). The artificial neural network can be a useful tool for clinical decision making and risk stratification of older adult patients with pneumonia in the ICU ( 21 ).…”

Section: Discussionmentioning

confidence: 99%

Risk factors analysis and prediction model construction for severe pneumonia in older adult patients

Liu,

Jiang,

Zhang

et al. 2024

Front. Public Health

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ObjectivePneumonia is a common and serious infectious disease that affects the older adult population. Severe pneumonia can lead to high mortality and morbidity in this group. Therefore, it is important to identify the risk factors and develop a prediction model for severe pneumonia in older adult patients.MethodIn this study, we collected data from 1,000 older adult patients who were diagnosed with pneumonia and admitted to the intensive care unit (ICU) in a tertiary hospital. We used logistic regression and machine learning methods to analyze the risk factors and construct a prediction model for severe pneumonia in older adult patients. We evaluated the performance of the model using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and calibration plot.ResultWe found that age, comorbidities, vital signs, laboratory tests, and radiological findings were associated with severe pneumonia in older adult patients. The prediction model had an accuracy of 0.85, a sensitivity of 0.80, a specificity of 0.88, and an AUC of 0.90. The calibration plot showed good agreement between the predicted and observed probabilities of severe pneumonia.ConclusionThe prediction model can help clinicians to stratify the risk of severe pneumonia in older adult patients and provide timely and appropriate interventions.

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Section: Discussionmentioning

confidence: 99%

Risk factors analysis and prediction model construction for severe pneumonia in older adult patients

Liu,

Jiang,

Zhang

et al. 2024

Front. Public Health

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Deep learning in fringe projection: A review

Liu,

Yan,

Shao

et al. 2024

Neurocomputing

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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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Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

Cited by 5 publications

References 94 publications

Risk factors analysis and prediction model construction for severe pneumonia in older adult patients

Risk factors analysis and prediction model construction for severe pneumonia in older adult patients

Deep learning in fringe projection: A review

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

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