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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">The article &#8220;Machine-learning models for prediction of sepsis patients mortality&#8221;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> is very interesting for many reasons&#58; the methodology used&#44; the adoption of advanced stadisticals technics&#44; the huge sample size&#44; and the aim of universalize the results comparing different populations&#46; However&#44; for us&#44; it is remarkable that they do not emphasize the struggle between the clinical prediction and the model prediction&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">QuickSOFA &#40;QS&#41; scale intend to predict mortality as a brief version of a more complex scale&#44; thresh from the Sepsis-3 recommendations&#46; It is based on 3 variables&#58; respiratory rate &#40;RR&#41;&#44; neurological impairment &#40;following the Glasgow coma scale&#58; GCS&#41;&#44; and systolic blood pressure &#40;SBP&#41;<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>100<span class="elsevierStyleHsp" style=""></span>mmHg&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">As it is said in the paper&#44; the SOFA scale had not had the good clinical prediction that it has in the model&#46; In the search of why this could happen&#44; if we focused on the variables included&#44; they were some of the least important in the predictive model&#58; RR &#40;13th out of 15&#41;&#44; GCS &#40;9th out of 15&#41; and SBP &#40;15th out of 15&#41;&#46; The most exciting point is that there are other accessible and more valuable variables for the model at our reach&#44; as age&#44; median blood pressure&#44; and temperature&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The interesting thing is that there are easily accessible variables that are more relevant to the model&#44; such as age&#44; mean blood pressure&#44; and temperature&#44; than those used by QS&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">The importance of variables is not entirely equivalent to the punctuation in the logistic regression results&#59; it is more related to the need for that variable for decision-making by the algorithm&#46; Therefore&#44; a frequently used variable is relevant when predicting and a less frequently used variable does not contribute as much&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Here lies the crucial issue&#46; Traditionally&#44; we have made use of predictors based on linear statistical models &#40;multivariate logistic regression&#41; that have offered us predictive variables which allow us to detect and grade the risk of patients in an objective manner&#46; Currently&#44; machine learning techniques - based on non-linear statistical models &#8211; grant us a greater predictive capacity with a different priority of variables than we knew&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">This represents a key change&#44; as it not only implies the generation of &#8220;new and different prediction for the same patient&#44; but also forces us to think of new predictors for diseases that we already thought we understood enough&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> The predictive success of these models should encourage us to create datasets &#40;collaborative databases&#41; that allow us to exploit their predictive and hierarchical potential of the variables employed&#44; which would lead us to prioritize areas of knowledge in diseases that we may have been neglecting&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">We don&#39;t just have traditional statistics&#44; but we have a multi-purpose tool machine learning&#46; Our responsibility is to know how to use all the tools we have properly&#44; to identify the problem we want to solve and therefore choose the appropriate statistical tool&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> This is not an invitation to give up on tradition&#44; but rather a need to include new statistical tools to improve our knowledge in a complex clinical scenario&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Financing</span><p id="par0045" class="elsevierStylePara elsevierViewall">None&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Conflicts of interest</span><p id="par0050" class="elsevierStylePara elsevierViewall">The authors declare that none have conflicts of interest&#46;</p></span></span>"
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Letter to the Editor
Machine-learning models for prediction of sepsis patients mortality: A needed consideration
Predicción de mortalidad en pacientes sépticos mediante modeles de machine-learning: una reflexión necesaria
Marcos Valiente Fernández
Autor para correspondencia
mvalientefernandez@gmail.com

Corresponding author.
, Francisco de Paula Delgado Moya, Amanda Lesmes González de Aledo, Isaías Martín Badía
Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Avenida de Córdoba, s/n, 28041, Madrid, Spain
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GCS&#41;&#44; and systolic blood pressure &#40;SBP&#41;<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>100<span class="elsevierStyleHsp" style=""></span>mmHg&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">As it is said in the paper&#44; the SOFA scale had not had the good clinical prediction that it has in the model&#46; In the search of why this could happen&#44; if we focused on the variables included&#44; they were some of the least important in the predictive model&#58; RR &#40;13th out of 15&#41;&#44; GCS &#40;9th out of 15&#41; and SBP &#40;15th out of 15&#41;&#46; The most exciting point is that there are other accessible and more valuable variables for the model at our reach&#44; as age&#44; median blood pressure&#44; and temperature&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The interesting thing is that there are easily accessible variables that are more relevant to the model&#44; such as age&#44; mean blood pressure&#44; and temperature&#44; than those used by QS&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">The importance of variables is not entirely equivalent to the punctuation in the logistic regression results&#59; it is more related to the need for that variable for decision-making by the algorithm&#46; Therefore&#44; a frequently used variable is relevant when predicting and a less frequently used variable does not contribute as much&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Here lies the crucial issue&#46; Traditionally&#44; we have made use of predictors based on linear statistical models &#40;multivariate logistic regression&#41; that have offered us predictive variables which allow us to detect and grade the risk of patients in an objective manner&#46; Currently&#44; machine learning techniques - based on non-linear statistical models &#8211; grant us a greater predictive capacity with a different priority of variables than we knew&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">This represents a key change&#44; as it not only implies the generation of &#8220;new and different prediction for the same patient&#44; but also forces us to think of new predictors for diseases that we already thought we understood enough&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> The predictive success of these models should encourage us to create datasets &#40;collaborative databases&#41; that allow us to exploit their predictive and hierarchical potential of the variables employed&#44; which would lead us to prioritize areas of knowledge in diseases that we may have been neglecting&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">We don&#39;t just have traditional statistics&#44; but we have a multi-purpose tool machine learning&#46; Our responsibility is to know how to use all the tools we have properly&#44; to identify the problem we want to solve and therefore choose the appropriate statistical tool&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> This is not an invitation to give up on tradition&#44; but rather a need to include new statistical tools to improve our knowledge in a complex clinical scenario&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Financing</span><p id="par0045" class="elsevierStylePara elsevierViewall">None&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Conflicts of interest</span><p id="par0050" class="elsevierStylePara elsevierViewall">The authors declare that none have conflicts of interest&#46;</p></span></span>"
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