Improving ED admissions forecasting by using generative AI: An approach based on DGAN

Computer Methods and Programs in Biomedicine, vol. 256, pp. 108363

Abstract

BACKGROUND AND OBJECTIVE: Generative Deep Learning has emerged in recent years as a significant player in the Artificial Intelligence field. Synthesizing new data while maintaining the features of reality has revolutionized the field of Deep Learning, proving to be particularly useful in contexts where obtaining data is challenging. The objective of this study is to employ the DoppelGANger algorithm, a cutting-edge approach based on Generative Adversarial Networks for time series, to enhance patient admissions forecasting in a hospital Emergency Department. METHODS: We employed the DoppelGANger algorithm in a sequential methodology, conditioning generated time series with unique attributes to optimize data utilization. After confirming the successful creation of synthetic data with new attribute values, we adopted the Train-Synthetic-Test-Real framework to ensure the reliability of our synthetic data validation. We then augmented the original series with synthetic data to enhance the Prophet model's performance. This process was applied to two datasets derived from the original: one with four years of training followed by one year of testing, and another with three years of training and two years of testing. RESULTS: The experimental results show that the generative model outperformed Prophet on the forecasting task, improving the SMAPE from 7.30 to 6.99 with the four-year training set, and from 22.84 to 7.41 for the three-year training set, all in daily aggregations. For the data replacement task, the Prophet SMAPE values decreased to 6.84 and 7.18 for four and three-year sets on the same aggregation. Additionally, data augmentation reduced the SMAPE to 6.79 for a one-year test set and achieved 8.56 for the two-year test set, surpassing the performance achieved by the same Prophet model when trained only on real data. Results for the remaining aggregations were consistent. CONCLUSIONS: The findings of this study suggest that employing a generative algorithm to extend a training dataset can effectively enhance predictive models within the domain of Emergency Department admissions. The improvement can lead to more efficient resource allocation and patient management.

Authors 3

1. Hugo Álvarez-Chaves corresponding

   Universidad de Alcalá

   Affiliation as printed

   Universidad de Alcalá, Escuela Politécnica Superior, 28805, Madrid, Spain. Electronic address: hugo.alvarezc@uah.es

   Universidad de Alcalá, Escuela Politécnica Superior, 28805, Madrid, Spain

2. Marco René Spruit Aachen Department of Public Health and Primary Care

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Department of Public Health and Primary Care, 2333 ZA, Leiden, The Netherlands. Electronic address: m.r.spruit@liacs.leidenuniv.nl

   Leiden University Medical Center, Department of Public Health and Primary Care, 2333 ZA, Leiden, The Netherlands

3. Maria Dolores R-Moreno

   Universidad de Alcalá

   Affiliation as printed

   Universidad de Alcalá, Escuela Politécnica Superior, 28805, Madrid, Spain. Electronic address: malola.rmoreno@uah.es

   Universidad de Alcalá, Escuela Politécnica Superior, 28805, Madrid, Spain