Forecasting of Outpatient Hospital Visits using A Bidirectional Long Short-Term Memory Model

Recently, accurate predicting outpatient visits is crucial in healthcare for optimizing service delivery and resource allocation. Time series models, particularly Deep Learning (DL) methods, have gained popularity in predicting demand and can potentially be used to predict demand for medical services, including outpatient hospital visits. This study aims to assess the potential of Bidirectional Long Short-Term Memory (Bi-LSTM) model in accurately predicting outpatient visits. The proposed model was tested with different set of parameters. Two important parameters adjusted in this study which are batch size and number of hidden nodes. The study also compares the performance of Bi-LSTM with other LSTM architectures, such as Vanilla LSTM and Stack LSTM. The results obtained show that Bi-LSTM performs best with batch size 64 and 10 hidden neurons. Besides, the results yielded indicate that the Bi-LSTM model performs exceptionally well with higher accuracy compared to other LSTM architectures. Overall, this study offers valuable insights into the use of Bi-LSTM models for predicting the number of outpatient visits.