Outpatient number prediction method and system based on automatic deep belief network

Abstract

The invention discloses an outpatient number prediction method and system based on an automatic deep belief network. The everyday outpatient number is collected from a hospital registration system to obtain historical outpatient number data, differential transformation pretreatment is carried out on the historical outpatient number data to obtain differential data, a deep belief network structure is automatically constructed in dependence on the differential data, groups are automatically created by means of a clustering algorithm to obtain grouped data in different time series, then the deep belief network is trained in dependence on the grouped data to obtain an outpatient number prediction model, finally the outpatient number prediction model is called to predict the outpatient number in a designated time series to obtain a prediction result, and inverse transformation of the pretreatment is carried out on the prediction result to obtain the predicted outpatient number. The deep belief network is advantaged by being convenient to use and simple to train, and can provide a reliable basis for the prediction of the hospital outpatient number, and is small in prediction error, and is especially suitable for long-term prediction.

Description

technical field [0001] The invention relates to the field of smart medical technology, in particular to a method for predicting outpatient volume based on an automatic deep belief network and a system for applying the method. Background technique [0002] Outpatient volume prediction is of great significance for improving medical efficiency and medical quality, especially for large general hospitals, scientific prediction and accurate analysis of dynamic changes in hospital outpatient volume can provide decision-making basis for hospital leaders to formulate outpatient work plans and arrange medical staff as a whole , which in turn can reduce the waiting time of patients, improve work efficiency, economic benefits and social benefits. [0003] However, it is extremely difficult to accurately predict the outpatient volume of a hospital. The outpatient volume of a hospital is closely related to many factors such as seasonal changes and climate changes. Therefore, the outpatie...

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Problems solved by technology

[0003] However, it is extremely difficult to accurately predict the outpatient volume of a hospital

The outpatient volume of a hospital is closely related to many factors such as seasonal changes and climate changes. Therefore, the outpatient volume data has a highly nonlinear nature, which makes traditional linear or probability models unable to show its effectiveness in dealing with sudden disease prediction / seasonality good effect of disease

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