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 circulatory-related 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 spatio-temporal 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.

空气污染和空气中的花粉在呼吸系统和循环系统疾病中起关键作用，因此与这些原因导致的住院人数直接相关。预先了解患者涌入急诊服务的情况，使临床机构可以优化资源并改善其服务。由于影响呼吸系统和循环系统相关医院入院的变量属于航空生物学或气象学领域，因此我们的目标是建立一个基于数据的系统，该系统无需先验假设即可预测出院率。给定观测站（气象站，花粉站，化学污染站和医院）的数量和分布，以前的方法会生成许多模型相关的系统，需要对其进行组合才能完全代表未来的环境条件。能够提取所有时间动态以及所有空间关系的统一方法将可以更好地表示上述情况，因此可以更精确地预测住院人数。提出的系统基于长短期记忆和卷积神经网络的特定神经网络拓扑，以获取所有独立变量和目标变量之间的时空关系。它被用于预测由于呼吸系统和循环系统相关疾病而导致的每日住院人数。该提案通过将循环和呼吸系统案例的预测误差平均分别降低28％和20％，从而优于基准方法。因此，该系统无需特定的现场知识即可提取所有相关信息，并提供准确的住院人数预测。