ABSTRACT
To compare the performance of generalized additive model (GAM) and long short-term memory recurrent neural network (LSTM-RNN) on the prediction of daily admissions of respiratory diseases with comorbid diabetes. Daily data on air pollutants, meteorological factors and hospital admissions for respiratory diseases from Jan 1st, 2014 to Dec 31st, 2019 in Beijing were collected. LSTM-RNN was used to predict the daily admissions of respiratory diseases with comorbid diabetes, and the results were compared with those of GAM. The evaluation indexes were calculated by five-fold cross validation. Compared with the GAM, the prediction errors of LSTM-RNN were significantly lower [root mean squared error (RMSE): 21.21±3.30 vs. 46.13±7.60, <0.01; mean absolute error (MAE): 14.64±1.99 vs. 36.08±6.20, <0.01], and the value was significantly higher (0.79±0.06 vs. 0.57±0.12, <0.01). In gender stratification, RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting female admission (all <0.05), but there were no significant difference in predicting male admission between two models (all >0.05). In seasonal stratification, RMSE and MAE of LSTM-RNN were lower than those of GAM in predicting warm season admission (all <0.05), but there was no significant difference in value (>0.05). There were no significant difference in RMSE, MAE and between the two models in predicting cold season admission (all >0.05). In the stratification of functional areas, the RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting core area admission (all <0.05). has lower prediction errors and better fitting than the GAM, which can provide scientific basis for precise allocation of medical resources in polluted weather in advance.
Subject(s)
Female , Humans , Male , Beijing/epidemiology , Diabetes Mellitus/epidemiology , Hospitalization , Memory, Short-Term , Neural Networks, ComputerABSTRACT
This paper analyzed the feasibility of SPOC-mixed teaching in view of bottlenecks faced by the current health statistics curriculum, and deeply analyzed the limitations of traditional classroom teaching and network teaching only, as well as the advantages of SPOC-mixed teaching mode. At the same time, the construction of SPOC-mixed teaching mode of health statistics curriculum was explored from three aspects: teaching preparation, teaching implementation and teaching evaluation. It is hoped that the traditional teaching mode of "mainly teaching' "existed in health statistics will be transformed into a student-led and learning-based mode. According to each student's learning levels, professional background and cognitive style, individualized teaching was conducted to teach students in accordance with their ability, and promote the reform on traditional education concepts and teaching modes of health statistics.
ABSTRACT
In order to find out the social attitude towards euthanasia and related issues,we conduct a questionnaire investigation on medical staff,patients,medical school students,and community groups in Beijing.From the survey,we find out that there is a relatively high degree of awareness of euthanasia,and the different attitudes for euthanasia are relevant to the educational backgrounds and professional backgrounds.In addition,by analyzing the mentality of people talking about the death,we find out that people with a healthy outlook on life and death are more inclined to agree with active euthanasia.