Statistical Analysis and Identification of Important Factors of Liver Disease using Machine Learning and Deep Learning Architecture

Islam, Md. Kabirul; Alam, Mirza Mohtashim; Rony, Rashad Al Hasan; Mohiuddin, Karishma

doi:10.1145/3319921.3319929

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…We also labeled the severity of patients as 1 for mild syndrome, 2 for severe syndrome, and 3 for critical syndrome in the correlation coefficient analysis [28]. Then, all data were put into one file to calculate the Spearman correlation coefficient (R, 3.6.1, package 'PerformanceAnalytics') and random forest model (Python 3.7) according to previous reports [29,30]. Briefly, we used the severity of patients as 1 for mild syndrome, 2 for severe syndrome, and 3 for critical syndrome as the outcome variable for the random forest model.…”

Section: Spearman Correlation Coefficient and Random Forest Model Anamentioning

confidence: 99%

Autopsy and statistical evidence of disturbed hemostasis progress in COVID-19: medical records from 407 patients

Jiang

Liu

et al. 2021

Thrombosis J

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Background The progression of coagulation in COVID-19 patients with confirmed discharge status and the combination of autopsy with complete hemostasis parameters have not been well studied. Objective To clarify the thrombotic phenomena and hemostasis state in COVID-19 patients based on epidemiological statistics combining autopsy and statistical analysis. Methods Using autopsy results from 9 patients with COVID-19 pneumonia and the medical records of 407 patients, including 39 deceased patients whose discharge status was certain, time-sequential changes in 11 relevant indices within mild, severe and critical infection throughout hospitalization according to the Chinese National Health Commission (NHC) guidelines were evaluated. Statistical tools were applied to calculate the importance of 11 indices and the correlation between those indices and the severity of COVID-19. Results At the beginning of hospitalization, platelet (PLT) counts were significantly reduced in critically ill patients compared with severely or mildly ill patients. Blood glucose (GLU), prothrombin time (PT), activated partial thromboplastin time (APTT), and D-dimer levels in critical patients were increased compared with mild and severe patients during the entire admission period. The International Society on Thrombosis and Haemostasis (ISTH) disseminated intravascular coagulation (DIC) score was also high in critical patients. In the relatively late stage of nonsurvivors, the temporal changes in PLT count, PT, and D-dimer levels were significantly different from those in survivors. A random forest model indicated that the most important feature was PT followed by D-dimer, indicating their positive associations with disease severity. Autopsy of deceased patients fulfilling diagnostic criteria for DIC revealed microthromboses in multiple organs. Conclusions Combining autopsy data, time-sequential changes and statistical methods to explore hemostasis-relevant indices among the different severities of the disease helps guide therapy and detect prognosis in COVID-19 infection.

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Section: Spearman Correlation Coefficient and Random Forest Model Anamentioning

confidence: 99%

Autopsy and statistical evidence of disturbed hemostasis progress in COVID-19: medical records from 407 patients

Jiang

Liu

et al. 2021

Thrombosis J

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show abstract

“…Then all data were put into one le to calculate the Pearson correlation coe cient (R, 3.6.1, package 'gpairs') and random forest model (Python 3.7) according to previous reports. 26…”

Section: Pearson Correlation Coe Cient and Random Forest Model Analysismentioning

confidence: 99%

Autopsy and Informatics Analysis Evidence Coagulopathy Progress in COVID-19 Patients

Jiang¹,

Lv²,

Liu³

et al. 2020

Preprint

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Background The outbreak of COVID-19 around the world resulted in more than 480 thousand deaths. Objective To clarify the thrombotic phenomena with coagulation progress in COVID-19 patients based on epidemiological statistics combining the autopsy and informatics analysis. Methods Using 9 autopsy results with COVID-19 pneumonia and the medical records of 407 patients including 39 deceased ones whose discharge status was certain, time-sequential changes of 11 coagulation relevant indices within mild, severe and critical infection throughout hospitalization according to NHC guidelines were evaluated. Informatics tools were applied to calculate the importance and correlation between them and the progression of thrombosis. Results At the beginning of the hospitalization, PLT had a significant decrease in critically ill patients. GLU, PT, APTT, and D-dimer in critical patients were higher than those in mild and severe during the whole admission period. The ISTH DIC score also showed the continuous overt DIC in critical patients. At the late stage of non-survivors, the dynamic profiles of PLT, PT, and D-dimer were significantly different from survivors. A random forest model indicated that the most important feature was PT, followed by D-dimer, indicating their crucial roles for the progression of disease. Conclusions COVID-19 is constantly spreading wildly around the world, combining autopsy data, time-sequential profiles and informatics methods to explore the dynamic changes of coagulation relevant indices throughout the course of the disease deterioration, which helps guide the therapy and detect the prognosis in different level of COVID-19 infection.

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“…Then all data were put into one le to calculate the Pearson correlation coe cient (R, 3.6.1, package 'gpairs') and random forest model (Python 3.7) according to previous reports. 28,29 Brie y, when constructing the model, the decision tree is enerated by CART algorithm using Gini index (also identi ed as importance index in our study). Gini index represents the impurity level of the model and the Gini index is smaller, the lower purity.…”

Section: Pearson Correlation Coe Cient and Random Forest Model Analysismentioning

confidence: 99%

Autopsy and Informatics Analysis Evidence Disturbed Haemostasis Progress in COVID-19: Medical Records from 407 Patients

Jiang¹,

Liu

et al. 2020

Preprint

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Background: The progress of coagulation in COVID-19 patients with confirmed discharge status (decease or discharge) and the combination of the autopsy with the complete coagulation parameters were not well studied.Objective: To clarify the thrombotic phenomena with coagulation progress in COVID-19 patients based on epidemiological statistics combining the autopsy and informatics analysis.Methods: Using 9 autopsy results with COVID-19 pneumonia and the medical records of 407 patients including 39 deceased ones whose discharge status was certain, time-sequential changes of 11 relevant indices within mild, severe and critical infection throughout hospitalization according to the Chinese National Health Commission (NHC) guidelines were evaluated. Informatics tools were applied to calculate the importance of 11 indices and the correlation between those indices and the severities of COVID-19.Results: At the beginning of the hospitalization, platelet (PLT) had a significant decrease in critically ill patients. Blood glucose (GLU), prothrombin time (PT), activated partial thromboplastin time (APTT), and D-dimer in critical patients were higher than those in mild and severe during the whole admission period. The International Society on Thrombosis and Haemostasis (ISTH) disseminated intravascular coagulation (DIC) score also showed the high DIC level in critical patients. At the relatively late stage of non-survivors, the temporal changes of PLT count, PT, and D-dimer were significantly different from survivors. A random forest model indicated that the most important feature was PT, followed by D-dimer, indicating their positive associations for the severities of disease. Autopsy data from 9 deceased patients also showed the DIC phenomena with prolonged PT, APTT, less PLT count and thrombosis in multiple organs.Conclusions: Combining autopsy data, time-sequential changes and informatics methods to explore the coagulation relevant indices among the different severities of the disease, helps guide the therapy and detect the prognosis in COVID-19 infection.

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Statistical Analysis and Identification of Important Factors of Liver Disease using Machine Learning and Deep Learning Architecture

Cited by 5 publications

References 14 publications

Autopsy and statistical evidence of disturbed hemostasis progress in COVID-19: medical records from 407 patients

Autopsy and statistical evidence of disturbed hemostasis progress in COVID-19: medical records from 407 patients

Autopsy and Informatics Analysis Evidence Coagulopathy Progress in COVID-19 Patients

Autopsy and Informatics Analysis Evidence Disturbed Haemostasis Progress in COVID-19: Medical Records from 407 Patients

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