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

Candia-Véjar, Alfredo; Moya-Carvajal, Jonathan; Pérez-Galarce, Francisco; Taramasco, Carla; Astudillo, César A.

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

dc.contributor.author	Candia-Véjar, Alfredo	es
dc.contributor.author	Moya-Carvajal, Jonathan	es
dc.contributor.author	Pérez-Galarce, Francisco	es
dc.contributor.author	Taramasco, Carla	es
dc.contributor.author	Astudillo, César A.	es
dc.date.accessioned	2023-04-21T20:46:13Z
dc.date.available	2023-04-21T20:46:13Z
dc.date.issued	2023-03-02
dc.description.abstract	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.	en
dc.identifier.citation	Expert Systems With Applications, Vol. 223, N° 1 (2023)	en_US
dc.identifier.doi	https://doi.org/10.1016/j.eswa.2023.119864
dc.identifier.issn	0957-4174	en_US
dc.identifier.orcid	https://orcid.org/0000-0003-2953-6522
dc.identifier.uri	http://hdl.handle.net/20.500.12254/3227
dc.language.iso	en	en
dc.publisher	Elsevier	en_US
dc.rights	Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subject	Length-of-stay prediction	en_US
dc.subject	Applied machine learning	en_US
dc.subject	Text embeddings	en_US
dc.subject	Emergency units	en_US
dc.subject	Explanaible artificial intelligence	en_US
dc.title	ML models for severity classification and length-of-stay forecasting in emergency units	en_US
dc.type	Artículo	es