Author(s): Peixoto, Diogo ; Faria, Mariana ; Macedo, Rui ; Peixoto, Hugo ; Lopes, João ; Barbosa, Agostinho ; Guimarães, Tiago André Saraiva ; Santos, Manuel
Date: 2022
Persistent ID: https://hdl.handle.net/1822/89570
Origin: RepositóriUM - Universidade do Minho
Subject(s): Length of stay; Machine Learning; Predictive analytics
In recent years, hospital overcrowding has become a crucial aspect to take into consideration in inpatient management, which may negatively affect the quality of service provided to the patient. Inpatient management aims, through efficient planning, to maximise the availability of beds and conditions for the patient, considering cost rationalisation. In this way, this research has allowed the prediction of the length of stay (LOS) of each patient in the Internal Medicine specialty, with acuity, considering their demographic data, the information collected at the time of admission and clinical conditions, which may help health professionals in carrying out more assertive planning. For this study, were used data sets from the Centro Hospitalar do Tâmega e Sousa (CHTS), referring to a 5-year period, 2017 to 2021. The GB model achieved an accuracy of ≈96% compared to the DT, RF and KNN, proving that Machine Learning (ML) models, using demographic information simultaneously with the route taken by the patient and clinical data, such as drugs administrated, exams, surgeries and analyses, introduce a greater predictive capacity of the LOS.
