Deep learning architecture to predict daily hospital admissions
- PDF / 1,137,694 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 11 Downloads / 220 Views
(0123456789().,-volV)(0123456789(). ,- volV)
REVIEW ARTICLE
Deep learning architecture to predict daily hospital admissions Ricardo Navares1 • Jose´ L. Aznarte1 Received: 2 August 2019 / Accepted: 5 March 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Air pollution and airborne pollen play a key role in respiratory and circulatory disorders and thus have a direct relation to hospital admissions for these causes. Knowing in advance the influx of patients to emergency services allows clinical institutions to optimize resources and to improve their service. Since the variables influencing respiratory and circulatoryrelated hospital admissions belong to fields such aerobiology or meteorology, we aim for a data-based system which is able to predict admissions without a priori assumptions. Given the number and distribution of observation stations (meteorological, pollen and chemical pollution stations and hospital), previous approaches generate many model-dependent systems that need to be combined in order to obtain the full representation of future environmental conditions. A unified approach able to extract all temporal dynamics as well as all spatial relations would allow a better representation of the aforementioned conditions and consequently a more precise hospital admissions forecast. The proposed system is based on a specific neural network topology of long short-term memories and convolutional neural networks to obtain the spatiotemporal relations between all independent and target variables. It was applied to forecast daily hospital admissions due to respiratory- and circulatory-related disorders. The proposal outperforms the benchmark approaches by reducing as an average the prediction error by 28% and 20% for the circulatory and respiratory cases, respectively. Consequently, the system extracts all relevant information without specific field knowledge and provides accurate hospital admissions forecasts. Keywords Convolution Neural networks Forecasting Hospital admissions
1 Introduction During the last few decades, air pollution and allergens have been consistently linked with mortality [3, 10, 13, 37, 41, 47] due to its known effects on patients with respiratory and circulatory disorders [11, 12]. Forecasting these pathologies is a critical issue in order to apply preventive measures and to plan medical resources [4, 34] in order to avoid congestions and overcrowding emergency departments in hospitals [59]. Knowing in advance the influx of patients emergency admissions eases clinical institutions management in optimizing resources with the consequent economic implication [2, 8, 14, 16–19, 25, 39, 60]. Furthermore, improving the
& Ricardo Navares [email protected] 1
Department of Artificial Intelligence, UNED, Juan del Rosal, 16, 28040 Madrid, Spain
efficiency of resources is directly related to the improvement in patient care [46]. Air pollution and allergens are one of many environmental factors which play a causative role in the incidence of respiratory and cir
Data Loading...
