TY - JOUR T1 - Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit JO - Archivos de Bronconeumología T2 - AU - Heili-Frades,Sarah AU - Minguez,Pablo AU - Mahillo Fernández,Ignacio AU - Jiménez Hiscock,Luis AU - Santos,Arnoldo AU - Heili Frades,Daniel AU - Carballosa de Miguel,María del Pilar AU - Fernández Ormaechea,Itziar AU - Álvarez Suárez,Laura AU - Naya Prieto,Alba AU - González Mangado,Nicolás AU - Peces-Barba Romero,Germán SN - 03002896 M3 - 10.1016/j.arbres.2019.11.019 DO - 10.1016/j.arbres.2019.11.019 UR - https://www.archbronconeumol.org/en-patient-management-assisted-by-neural-articulo-S0300289619305940 AB - IntroductionMortality risk prediction for Intermediate Respiratory Care Unit's (IRCU) patients can facilitate optimal treatment in high-risk patients. While Intensive Care Units (ICUs) have a long term experience in using algorithms for this purpose, due to the special features of the IRCUs, the same strategics are not applicable. The aim of this study is to develop an IRCU specific mortality predictor tool using machine learning methods. MethodsVital signs of patients were recorded from 1966 patients admitted from 2007 to 2017 in the Jiménez Díaz Foundation University Hospital's IRCU. A neural network was used to select the variables that better predict mortality status. Multivariate logistic regression provided us cut-off points that best discriminated the mortality status for each of the parameters. A new guideline for risk assessment was applied and mortality was recorded during one year. ResultsOur algorithm shows that thrombocytopenia, metabolic acidosis, anemia, tachypnea, age, sodium levels, hypoxemia, leukocytopenia and hyperkalemia are the most relevant parameters associated with mortality. First year with this decision scene showed a decrease in failure rate of a 50%. ConclusionsWe have generated a neural network model capable of identifying and classifying mortality predictors in the IRCU of a general hospital. Combined with multivariate regression analysis, it has provided us with an useful tool for the real-time monitoring of patients to detect specific mortality risks. The overall algorithm can be scaled to any type of unit offering personalized results and will increase accuracy over time when more patients are included to the cohorts. ER -