Articles producció científica> Medicina i Cirurgia

Pursuing optimal prediction of discharge time in ICUS with machine learning methods

  • Identification data

    Identifier: imarina:5724907
    Handle: http://hdl.handle.net/20.500.11797/imarina5724907
  • Authors:

    Cuadrado D
    Riaño D
    Gómez J
    Bodí M
    Sirgo G
    Esteban F
    García R
    Rodríguez A
  • Others:

    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
  • Keywords:

    Computer Science (Miscellaneous),Computer Science, Artificial Intelligence,Computer Science, Theory & Methods,Theoretical Computer Science
    Model
    Length-of-stay
    Intensive care units
    Intelligent data analysis
    Discharge time prediction
    Data-driven models
    Artificial neural-networks
    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
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