Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)

Hopp, Daniel

doi:10.48550/arxiv.2106.08901

Cited by 1 publication

(1 citation statement)

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“…Because LSDM has the advantage of processing various types of data sets with different mixtures of frequencies in features or targets (Hopp, 2021), it has been widely applied to various research contexts. For example, it has been used to predict road traffic (Zhao et al, 2017), floods (Akbari Asanjan et al, 2018), the workload of a cloud datacenter and financial markets (Bukhari et al, 2020).…”

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confidence: 99%

Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

Jia

Zhu

et al. 2022

IMDS

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PurposeAbundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.Design/methodology/approachAs the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.FindingsThe empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.Practical implicationsWith these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.Originality/valueTo address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

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