Value of laboratory tests in COVID-19 hospitalized patients for clinical decision-makers: a predictive model, using data mining approach

Mousavi, Atefeh; Rezaei, Soheyla; Salamzadeh, Jamshid; Mirzazadeh, Ali; Peiravian, Farzad; Yousefi, Nazila

doi:10.21203/rs.3.rs-56252/v1

Cited by 2 publications

(4 citation statements)

References 30 publications

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“…In contrast to the present study, which pre-dicted mortality of COVID-19 patients based on symptoms, signs, laboratory tests, and demographic characteristics, Mousavi et al [14] identified high-risk COVID-19 patients based on only laboratory tests. In this study, the sample size (n=4542) was higher than the present study.…”

Section: Discussioncontrasting

confidence: 73%

“…However, unlike the present study, in which all people with COVID-19 disease were included at any age, in this study, only the COVID-19 patients aged over 18 years were studied. Also, in contrast to the present study, which used four different data mining models, in the study conducted by Mousavi et al [14], logistic regression was used to compare recovered and dead patients in the SPSS environment. The highest accuracy in the present study was reported at 99.14% using the KNN2 algorithm.…”

Section: Discussionmentioning

confidence: 92%

“…In reviewing the above studies, we found that none of these studies focused on predicting mortality in patients with COVID-19. These studies have been conducted with different aims of identifying patients with COVID-19 infection [13], predicting death in patients with suspected sepsis [22], and identifying stress symptoms in people due to outbreak of COVID-19 [21], and patients with high-risk COVID-19 [14] and predicting the rate of CO-VID-19 in the next two weeks [18]. Regarding the importance of predicting patients' mortality, Deschepper et al [23] stated that early prediction of mortality in hospitals can improve the patient outcomes and enable health care providers to take adequate and timely action to save lives.…”

Section: Discussionmentioning

confidence: 99%

“…Step 3: Implementing the selected data mining techniques In this step, to select data mining models for data analysis, various studies in this field were reviewed [12][13][14]. According to the studies and the type and quality of data, appropriate models were selected.…”

Section: Step1: Data Identificationmentioning

confidence: 99%

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Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

Moulaei

2021

J Biomed Phys Eng

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If Coronavirus (COVID-19) is not predicted, managed, and controlled timely, the health systems of any country and their people will face serious problems. Predictive models can be helpful in health resource management and prevent outbreak and death caused by COVID-19. The present study aimed at predicting mortality in patients with COVID-19 based on data mining techniques. To do this study, the mortality factors of COVID-19 patients were first identified based on different studies. These factors were confirmed by specialist physicians. Based on the confirmed factors, the data of COVID-19 patients were extracted from 850 medical records. Decision tree (J48), MLP, KNN, random forest, and SVM data mining models were used for prediction. The models were evaluated based on accuracy, precision, specificity, sensitivity, and the ROC curve. According to the results, the most effective factor used to predict the death of COVID-19 patients was dyspnea. Based on ROC (1.000), accuracy (99.23%), precision (99.74%), sensitivity (98.25%) and specificity (99.84%), the random forest was the best model in predicting of mortality than other models. After the random forest, KNN5, MLP, and J48 models were ranked next, respectively. Data analysis of COVID-19 patients can be a suitable and practical tool for predicting the mortality of these patients. Given the sensitivity of medical science concerning maintaining human life and lack of specialized human resources in the health system, using the proposed models can increase the chances of successful treatment, prevent early death and reduce the costs associated with long treatments for patients, hospitals and the insurance industry.

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Section: Discussioncontrasting

confidence: 73%

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Step1: Data Identificationmentioning

confidence: 99%

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Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

Moulaei

2021

J Biomed Phys Eng

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Prognostic models in COVID-19 infection that predict severity: a systematic review

et al. 2023

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Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

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Value of laboratory tests in COVID-19 hospitalized patients for clinical decision-makers: a predictive model, using data mining approach

Cited by 2 publications

References 30 publications

Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

Prognostic models in COVID-19 infection that predict severity: a systematic review

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