In the last years, the Emergency Department (ED) has become an important source of admissions for hospitals. Since late 90s, the number of ED visits has been steadily increasing, and since Covid19 pandemic this trend has been much stronger. Accurate prediction of ED visits, even for moderate forecasting time-horizons, can definitively improve operational efficiency, quality of care, and patient outcomes in hospitals. In this paper we propose two different interpretable approaches, based on Machine Learning algorithms, to accurately forecast hospital emergency visits. The proposed approaches involve a first step of data segmentation based on two different criteria, depending on the approach considered: first, a threshold-based strategy is adopted, where data is divided depending on the value of specific predictor variables. In a second approach, a cluster-based ensemble learning is proposed, in such a way that a clustering algorithm is applied to the training dataset, and ML models are then trained for each cluster. The two proposed methodologies have been evaluated in real data from two hospital ED visits datasets in Spain. We have shown that the proposed approaches are able to obtain accurate ED visits forecasting, in short-term and also long-term prediction time-horizons up to one week, improving the efficiency of alternative prediction methods for this problem. The proposed forecasting approaches have a strong emphasis on providing explainability to the problem. An analysis on which variables govern the problem and are pivotal for obtaining accurate predictions is finally carried out and included in the discussion of the paper.

Prediction problems in timed datasets related to human activities are especially difficult to solve, because of the specific characteristics and the scarce number of predictive (input) variables available to tackle these problems. In this paper, we try to find out whether Machine Learning (ML) approaches can be successfully applied to these problems. We deal with timed datasets with human activity patterns, in which the input variables are exclusively related to the day or type of day when the prediction is carried out and, usually, to the meteorology of those days. These problems with a marked human activity pattern frequently appear in mobility and traffic-related problems, delivery prediction (packets, food), and many other activities, usually in cities. We evaluate the performance in these problems of different ML methods such as artificial neural networks (multi-layer perceptrons, extreme learning machines) and support vector regression algorithms, together with an Analogue-type (KNN) approach, which serves as a baseline algorithm and provides information about when it is expected that ML approaches will fail, by looking for similar situations in the past. The considered ML algorithms are evaluated in four real prediction problems with human activity patterns, such as school absences, bike-sharing demand, parking occupation, and packets delivered in a post office. The results obtained show the good performance of the ML algorithms, revealing that they can deal with scarce information in all the problems considered. The results obtained have also revealed the importance of including meteorology as the input variables, showing that meteorology is frequently behind demand peaks or valleys in this kind of problem. Finally, we show that having a number of similar situations in the past (training set) prevents ML algorithms from making important mistakes in the prediction obtained.