A comparison of machine learning algorithms in predicting COVID-19 prognostics

Üstebay, Serpil; Sarmis, Abdurrahman; Kaya, Gulsum Kubra; Sujan, Mark-Alexander

doi:10.1007/s11739-022-03101-x

Cited by 29 publications

(15 citation statements)

References 61 publications

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“…Several authors have developed similar approaches involving different variables in the prediction of mortality; some of them include dyspnoea,41 42 BUN, platelet count,42 43 sex,42 age,41 43–46 cough,41 weight,44 cardiovascular disease,41 orotracheal intubation,41 and pleural effusion,41 44 respiratory rate,42 fraction of inspired oxygen,42 blood oxygen saturation42 43 pH,42 aspartate aminotransferase levels,42 estimated glomerular filtration rate,42 lymphocyte count,44 47 WCC,41 43 creatine46 lactic acid47 and serum calcium 47. However, most of their studies did not focus on the interaction of such variables on hospital admission, and their interpretability by clinicians is difficult.…”

Section: Discussionmentioning

confidence: 99%

Predictors of mortality in hospitalised patients with COVID-19: a 1-year case–control study

Camacho-Domínguez,

Rojas,

Herrán

et al. 2024

BMJ Open

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BackgroundA paucity of predictive models assessing risk factors for COVID-19 mortality that extend beyond age and gender in Latino population is evident in the current academic literature.ObjectivesTo determine the associated factors with mortality, in addition to age and sex during the first year of the pandemic.DesignA case–control study with retrospective revision of clinical and paraclinical variables by systematic revision of clinical records was conducted. Multiple imputations by chained equation were implemented to account for missing variables. Classification and regression trees (CART) were estimated to evaluate the interaction of associated factors on admission and their role in predicting mortality during hospitalisation. No intervention was performed.SettingHigh-complexity centre above 2640 m above sea level (masl) in Colombia.ParticipantsA population sample of 564 patients admitted to the hospital with confirmed COVID-19 by PCR. Deceased patients (n=282) and a control group (n=282), matched by age, sex and month of admission, were included.Main outcome measureMortality during hospitalisation.Main resultsAfter the imputation of datasets, CART analysis estimated 11 clinical profiles based on respiratory distress, haemoglobin, lactate dehydrogenase, partial pressure of oxygen to inspired partial pressure of oxygen ratio, chronic kidney disease, ferritin, creatinine and leucocytes on admission. The accuracy model for prediction was 80.4% (95% CI 71.8% to 87.3%), with an area under the curve of 78.8% (95% CI 69.63% to 87.93%).ConclusionsThis study discloses new interactions between clinical and paraclinical features beyond age and sex influencing mortality in COVID-19 patients. Furthermore, the predictive model could offer new clues for the personalised management of this condition in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Predictors of mortality in hospitalised patients with COVID-19: a 1-year case–control study

Camacho-Domínguez,

Rojas,

Herrán

et al. 2024

BMJ Open

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“…During the pandemic, we saw an explosion in the number of ML algorithms to support the diagnosis and treatment of COVID-19. Examples include the use of Deep Learning to develop algorithms for the identification of COVID-19 from chest X-rays and CT scans,17 for the identification of patients at risk of critical COVID-19-related disease progression18 and for the rapid triage of patients with COVID-19 19. However, in retrospect, there were few, if any, examples of successful clinical deployments of these algorithms 20.…”

Section: Making Ai Work In Practice Requires a Systems Approachmentioning

confidence: 99%

Digital health and care: emerging from pandemic times

Peek,

Sujan,

Scott

2023

BMJ Health Care Inform

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In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges and opportunities ahead by reviewing significant developments reported in the literature. As before, the three key areas we consider are digital transformation of services, realising the potential of artificial intelligence and wise data sharing to facilitate learning health systems. We conclude that the field of digital health has rapidly matured during the pandemic, but there are still major sociotechnical, evaluation and trust challenges in the development and deployment of new digital services.

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“…Ustebay et al. ( 2023 ) described the prognostic and diagnostic paradigms of COVID-19 to support clinical decision-making. They used eight ML algorithms and explained that the added tree and CatBoost classifiers outperformed other studied models.…”

Section: Literature Surveymentioning

confidence: 99%

Multivariate time series short term forecasting using cumulative data of coronavirus

Mishra¹,

Singh

Kumar

et al. 2023

Evolving Systems

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Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the respiratory system of humans. The epidemic-related data is collected regularly, which machine learning algorithms can employ to comprehend and estimate valuable information. The analysis of the gathered data through time series approaches may assist in developing more accurate forecasting models and strategies to combat the disease. This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Forecasting is conducted utilizing state-of-the-art mathematical and deep learning models for multivariate time series forecasting, including extended susceptible-exposed-infected-recovered (SEIR), long-short-term memory (LSTM), and vector autoregression (VAR). The SEIR model has been extended by integrating additional information such as hospitalization, mortality, vaccination, and quarantine incidences. Extensive experiments have been conducted to compare deep learning and mathematical models that enable us to estimate fatalities and incidences more precisely based on mortality in the eight most affected nations during the time of this research. The metrics like mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are employed to gauge the model’s effectiveness. The deep learning model LSTM outperformed all others in terms of forecasting accuracy. Additionally, the study explores the impact of vaccination on reported epidemics and deaths worldwide. Furthermore, the detrimental effects of ambient temperature and relative humidity on pathogenic virus dissemination have been analyzed.

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A comparison of machine learning algorithms in predicting COVID-19 prognostics

Cited by 29 publications

References 61 publications

Predictors of mortality in hospitalised patients with COVID-19: a 1-year case–control study

Predictors of mortality in hospitalised patients with COVID-19: a 1-year case–control study

Digital health and care: emerging from pandemic times

Multivariate time series short term forecasting using cumulative data of coronavirus

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