Author, as appears in the article.: Cuadrado D; Riaño D; Gómez J; Bodí M; Sirgo G; Esteban F; García R; Rodríguez A
Department: Medicina i Cirurgia
URV's Author/s: Bodi Saera, Maria Amparo / Gómez Alvarez, Josep / Riaño Ramos, David
Keywords: Model Length-of-stay Intensive care units Intelligent data analysis Discharge time prediction Data-driven models Artificial neural-networks
Abstract: © Springer Nature Switzerland AG 2019. In hospital intensive care units (ICU), patients are under continuous evaluation. One of the purposes of this evaluation is to determine the expected number of days to discharge. This value is important to manage ICUs. Some studies show that health care professionals are good at predicting short-term discharge times, but not as good at long-term predictions. Machine learning methods can achieve 1.79-day average prediction error. We performed a study on 3,787 patient-days in the ICU of the Hospital Joan XXIII (Spain) to obtain a data-driven model to predict the discharge time of ICU patients, in a daily basis. Our model, which is based on random forest technology, obtained an error of 1.34 days. We studied the progression of the model as more data are available and predicted that the number of instances required to reduce the error below one day is 4,745. When we trained the model with all the available data, we obtained a mean error of less than half a day with a coefficient of determination (R2) above 97% in their predictions on either ICU survivors and not survivors. Similar results were obtained differentiating by patients’ gender and age, confirming our approach as a good means to achieve optimal performance when more data will be available.
Thematic Areas: Theoretical computer science Saúde coletiva Química Psicología Planejamento urbano e regional / demografia Odontología Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Interdisciplinar Geografía Geociências General o multidisciplinar General computer science Farmacia Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Educação física Educação Direito Comunicació i informació Comunicação e informação Computer science, theory & methods Computer science, artificial intelligence Computer science (miscellaneous) Computer science (all) Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Artes Arquitetura, urbanismo e design Arquitetura e urbanismo Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
ISSN: 03029743
Author's mail: josep.gomez@urv.cat mariaamparo.bodi@urv.cat mariaamparo.bodi@urv.cat
Author identifier: 0000-0002-0573-7621 0000-0001-7652-8379 0000-0001-7652-8379
Record's date: 2023-07-31
Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Lecture Notes In Computer Science. 11526 LNAI 150-154
APA: Cuadrado D; Riaño D; Gómez J; Bodí M; Sirgo G; Esteban F; García R; Rodríguez A (2019). Pursuing optimal prediction of discharge time in ICUS with machine learning methods.
Article's DOI: 10.1007/978-3-030-21642-9_20
Entity: Universitat Rovira i Virgili
Journal publication year: 2019
Publication Type: Proceedings Paper