ML models for severity classification and length-of-stay forecasting in emergency units

Length-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital are crucial problems for public and private health networks. An accurate estimation of these parameters is essential for better planning resources, which are usually scarce. Although it is possible to find several works that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the classification of the patient's post-care destination and by up to 8% in the prediction of LoS in the hospital. This evidence encourages practitioners to use available text to anticipate the patient's need for hospitalization more accurately at the earliest stage of the care process.