g7wbhz3o

Property	Value
?:abstract	Background The second-wave of CVOID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully and high accuracy. Methods The current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluates the statistically significant risk factors, generate new combined variables and then supply these risk factors to deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients ([≥]18 years) admitted to the Aga Khan University Hospital with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID-19 on RT-PCR, had incomplete or missing health records. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models and others. Results A total of 1228 cases were diagnosed as a COVID-19 infection, we excluded 14 patients after the exclusion criteria and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional models (Neo-V) and Deep-FLAIM models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the curve of the receiver-operator curve of 88.5. Conclusion Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.
is ?:annotates of	<https://research.tib.eu/covid-19/entity/g7wbhz3o_hasAnnotation_C0020517> <https://research.tib.eu/covid-19/entity/g7wbhz3o_hasAnnotation_C0026837> <https://research.tib.eu/covid-19/entity/g7wbhz3o_hasAnnotation_C0036983> <https://research.tib.eu/covid-19/entity/g7wbhz3o_hasAnnotation_C1366752> <https://research.tib.eu/covid-19/entity/g7wbhz3o_hasAnnotation_C1421479> <https://research.tib.eu/covid-19/entity/g7wbhz3o_hasAnnotation_C3539125> <https://research.tib.eu/covid-19/entity/g7wbhz3o_hasAnnotation_C5203676>
?:creator	<https://research.tib.eu/covid-19/entity/Naseem%2C_M.%3B_Arshad%2C_H.%3B_Hashimi%2C_S._A.%3B_Irfan%2C_F.%3B_Ahmed%2C_F._S.>
?:doi	<https://research.tib.eu/covid-19/entity/10.1101/2020.12.13.20247254>
?:doi	10.1101/2020.12.13.20247254
?:license	medrxiv
?:pdf_json_files	document_parses/pdf_json/513dec303c3c587ae048679a6c25c3ff1d863c30.json
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:sha_id	<https://research.tib.eu/covid-19/entity/513dec303c3c587ae048679a6c25c3ff1d863c30>
?:source	MedRxiv; WHO
?:title	Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:year	2020-12-14

Property

Value

?:abstract

Background The second-wave of CVOID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully and high accuracy. Methods The current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluates the statistically significant risk factors, generate new combined variables and then supply these risk factors to deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients ([≥]18 years) admitted to the Aga Khan University Hospital with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID-19 on RT-PCR, had incomplete or missing health records. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models and others. Results A total of 1228 cases were diagnosed as a COVID-19 infection, we excluded 14 patients after the exclusion criteria and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional models (Neo-V) and Deep-FLAIM models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the curve of the receiver-operator curve of 88.5. Conclusion Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

is ?:annotates of