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Research on prediction of daily admissions of respiratory diseases with comorbid diabetes in Beijing based on long short-term memory recurrent neural network / 浙江大学学报·医学版
Journal of Zhejiang University. Medical sciences ; (6): 1-9, 2022.
Article in English | WPRIM | ID: wpr-928651
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.
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Full text: Available Index: WPRIM (Western Pacific) Main subject: Neural Networks, Computer / Diabetes Mellitus / Beijing / Hospitalization / Memory, Short-Term Type of study: Prognostic study Limits: Female / Humans / Male Country/Region as subject: Asia Language: English Journal: Journal of Zhejiang University. Medical sciences Year: 2022 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Neural Networks, Computer / Diabetes Mellitus / Beijing / Hospitalization / Memory, Short-Term Type of study: Prognostic study Limits: Female / Humans / Male Country/Region as subject: Asia Language: English Journal: Journal of Zhejiang University. Medical sciences Year: 2022 Type: Article