Objective: To predict the daily incidence and fatality rates based on long short-term memory(LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran.Methods: To predict the daily incidence and fatality...Objective: To predict the daily incidence and fatality rates based on long short-term memory(LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran.Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM model. All data of COVID-19 disease were collected daily for training the LSTM model from February 22, 2020 to April 10, 2021 in the Mazandaran University of Medical Sciences. We defined 4 age groups, i.e., patients under 29, between 30 and 49, between 50 and 59, and over 60 years old. Then, LSTM models were applied to predict the trend of daily incidence and fatality rates from 14 to 40 days in different age groups. The results of different methods were compared with each other.Results: This study evaluated 5 0826 patients and 5 109 deaths with COVID-19 daily in 20 cities of Mazandaran Province. Among the patients, 25 240 were females(49.7%), and 25 586 were males(50.3%). The predicted daily incidence rates on April 11, 2021 were 91.76, 155.84, 150.03, and 325.99 per 100 000 people, respectively;for the fourteenth day April 24, 2021, the predicted daily incidence rates were 35.91, 92.90, 83.74, and 225.68 in each group per 100 000 people. Furthermore, the predicted average daily incidence rates in 40 days for the 4 age groups were 34.25, 95.68, 76.43, and 210.80 per 100 000 people, and the daily fatality rates were 8.38, 4.18, 3.40, 22.53 per 100 000 people according to the established LSTM model. The findings demonstrated the daily incidence and fatality rates of 417.16 and 38.49 per 100 000 people for all age groups over the next 40 days. Conclusions: The results highlighted the proper performance of the LSTM model for predicting the daily incidence and fatality rates. It can clarify the path of spread or decline of the COVID-19 outbreak and the priority of vaccination in age groups.展开更多
Objective:To investigate the economic burden of patients with acute kidney injury(AKI)by analyzing the distribution of hospitalization expenses and its influencing factors in the Affiliated Hospital of Hebei Universit...Objective:To investigate the economic burden of patients with acute kidney injury(AKI)by analyzing the distribution of hospitalization expenses and its influencing factors in the Affiliated Hospital of Hebei University.Methods:The hospitalization information of patients with AKI from January 2020 to January 2023 was collected and sorted through the hospital charging system and the factors affecting the total hospitalization cost were analyzed by multiple linear regression.Results:Univariate analysis showed that age,occupation,marriage,length of hospitalization,recovery of renal function,and stage of AKI had significant effects on hospitalization cost(P<0.05).The result of the multiple linear regression analysis model showed that age(t=4.11,P<0.0001),length of hospitalization(t=16.10,P<0.0001),recovery of renal function(t=3.26,P<0.0001),AKI stage(t=5.23,P=0.002)are factors affecting the economic burden of patients with AKI.Conclusion:AKI patients should be managed according to age stratification to effectively control the progression of the disease and improve the quality of the medical services provided.This will reduce the economic burden of patients.展开更多
文摘Objective: To predict the daily incidence and fatality rates based on long short-term memory(LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran.Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM model. All data of COVID-19 disease were collected daily for training the LSTM model from February 22, 2020 to April 10, 2021 in the Mazandaran University of Medical Sciences. We defined 4 age groups, i.e., patients under 29, between 30 and 49, between 50 and 59, and over 60 years old. Then, LSTM models were applied to predict the trend of daily incidence and fatality rates from 14 to 40 days in different age groups. The results of different methods were compared with each other.Results: This study evaluated 5 0826 patients and 5 109 deaths with COVID-19 daily in 20 cities of Mazandaran Province. Among the patients, 25 240 were females(49.7%), and 25 586 were males(50.3%). The predicted daily incidence rates on April 11, 2021 were 91.76, 155.84, 150.03, and 325.99 per 100 000 people, respectively;for the fourteenth day April 24, 2021, the predicted daily incidence rates were 35.91, 92.90, 83.74, and 225.68 in each group per 100 000 people. Furthermore, the predicted average daily incidence rates in 40 days for the 4 age groups were 34.25, 95.68, 76.43, and 210.80 per 100 000 people, and the daily fatality rates were 8.38, 4.18, 3.40, 22.53 per 100 000 people according to the established LSTM model. The findings demonstrated the daily incidence and fatality rates of 417.16 and 38.49 per 100 000 people for all age groups over the next 40 days. Conclusions: The results highlighted the proper performance of the LSTM model for predicting the daily incidence and fatality rates. It can clarify the path of spread or decline of the COVID-19 outbreak and the priority of vaccination in age groups.
文摘Objective:To investigate the economic burden of patients with acute kidney injury(AKI)by analyzing the distribution of hospitalization expenses and its influencing factors in the Affiliated Hospital of Hebei University.Methods:The hospitalization information of patients with AKI from January 2020 to January 2023 was collected and sorted through the hospital charging system and the factors affecting the total hospitalization cost were analyzed by multiple linear regression.Results:Univariate analysis showed that age,occupation,marriage,length of hospitalization,recovery of renal function,and stage of AKI had significant effects on hospitalization cost(P<0.05).The result of the multiple linear regression analysis model showed that age(t=4.11,P<0.0001),length of hospitalization(t=16.10,P<0.0001),recovery of renal function(t=3.26,P<0.0001),AKI stage(t=5.23,P=0.002)are factors affecting the economic burden of patients with AKI.Conclusion:AKI patients should be managed according to age stratification to effectively control the progression of the disease and improve the quality of the medical services provided.This will reduce the economic burden of patients.
