Abstract
In order to study the construction method of long- and short-term memory neural network model, which is based on particle swarm optimization algorithm and its application in hospital outpatient management, we have selected historical data of outpatient volume of relevant departments in our hospital. Furthermore, we have designed and developed the outpatient volume prediction model, which is based on long- and short-term memory neural network. Additionally, we have used particle swarm optimization algorithm (PSO) to optimize various parameters of long- and short-term memory network and then utilized this optimized model to accurately predict the outpatient volume. Experimental observations, which are collected through the results of monthly outpatient volume prediction, show that Root Mean Square Error (RMSE) of the particle swarm optimized LTMN model on the test set is reduced by 48.5% compared with the unoptimized model. The particle swarm optimization algorithm has efficiently optimized the prediction model, which makes the model better predict the trend of outpatient volume and thus provide decision support for medical staff's outpatient management.
Highlights
With the development of modern information technology, medical activities, medical research, and other process data are increasingly recorded and stored. ese advancements lead to the generation of huge amount of medical big data, which contain a large amount of valuable information, and it is difficult to effectively process it by traditional data processing methods [1,2,3,4]. e deep integration of artificial intelligence and medical field is one of the core technologies of medical information in the context of big data
We have proposed a process for optimizing key hyper parameters of long short-term memory (LSTM) using a simple and efficient particle swarm optimization (PSO) algorithm
In LSTM model, the number of neurons in each layer of the LSTM network is set as parameter ki, and these parameters are used as the candidate solution vectors for particle swarm optimization. e objective function is set as the outpatient volume prediction error of the LSTM model, and the prediction error is minimized by iterating the algorithm to find a set of optimal parameters of the model, and the LSTM model based on particle swarm optimization is obtained for the outpatient volume prediction task
Summary
With the development of modern information technology, medical activities, medical research, and other process data are increasingly recorded and stored. ese advancements lead to the generation of huge amount of medical big data, which contain a large amount of valuable information, and it is difficult to effectively process it by traditional data processing methods [1,2,3,4]. e deep integration of artificial intelligence and medical field is one of the core technologies of medical information in the context of big data. Reference [9] argued that deep learning-based time series data prediction model can map the data better; [11] used a combined model of kernel limit learning machine and least squares support vector machine to forecast monthly outpatient volume research; [2] used trend fitting and ARIMA to forecast combined outpatient volume; [13] formed a combined model of gray prediction model and ARIMA model after linear weights; [14] used gray model to fit monthly outpatient volume and used BP neural network model to predict outpatient volume by fitting residuals; [15] used seasonal differential autoregressive sliding average model with long- and short-term memory network to predict outpatient volume by fitting residuals; [16] used wavelet analysis and ARMA model to predict outpatient volume in winter in combination. Studies on the association between environment and specific disease outpatient visits usually make a predetermination based on experience, such as the association between respiratory disease outpatient visits and air pollution, and conduct a follow-up study
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