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Editorial
Big data: Value creation in clinical nutrition
Big data, creación de valor en nutrición clínica
Julia Alvarez Hernández
Sección de Endocrinología y Nutrición, Hospital Universitario Príncipe de Asturias, Universidad de Alcala, Alcalá de Henares, Madrid, Spain
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Artificial intelligence &#40;AI&#41; has made an unstoppable entry into our lives&#46; Its origins date back to the mathematical logic and computational works of Alan Turing<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">1</span></a> and McCulloch and Pitts&#44; published over 80 years ago&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">2</span></a> Artificial intelligence is a term coined by Minsky and McCarthy in 1956 at the Dartmouth conference&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">3</span></a> It is defined as the scientific field of computing that focuses on the creation of programs and mechanisms that can display behavior considered intelligent&#46; In other words&#44; &#8220;machines that think like human beings&#46;&#8221;</p><p id="par0010" class="elsevierStylePara elsevierViewall">But in order to develop the concept&#44; AI draws on a large amount of data &#40;big data &#91;BD&#93;&#41;&#44; and uses it to develop algorithms and generate its own logic&#46; Ultimately&#44; AI uses data to gain information from and interact with the environment accordingly&#46; Thus&#44; AI and BD are closely linked terms that offer opportunities for improvement in all disciplines if we are able to take advantage of the information systems available to us&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Big data is the term used in the information and communication technology sector in reference to a body of data which&#44; because of its volume and variety&#44; and the speed at which the data need to be processed&#44; exceeds the capabilities of standard computer systems&#46; Regarding Spanish&#44; it has been proposed that BD be translated as &#8220;macrodata&#8221;&#44; this being presented as a valid alternative&#44; since like the term &#8220;big&#8221;&#44; it refers to the concept of largeness&#46; As a solution it is to the point&#44; and unlike the term &#8220;megadata&#8221;&#44; it produces no confusion with &#8220;mega&#8221; &#8211; which is also often used in the same scenarios&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">4</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Big data implicates all aspects of human life&#44; including biology and medicine&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">5</span></a> The advances in recent decades in the world of &#8220;omics&#8221; &#40;genomics&#44; proteomics&#44; metabolomics&#41; and other types of technologies&#44; as well as the implementation of electronic health records &#40;EHRs&#41;&#44; have led to an exponential growth in data volume&#44; contributing to the reality of BD in the healthcare setting&#46; Today it constitutes a genuine augmentation of knowledge allowing us to innovate and improve the quality and efficiency of care&#46; According to Margolis&#44; &#8220;big data is not only a new reality for biomedical scientists&#44; but also an imperative that must be understood and used effectively in the search for new knowledge&#8221;&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">6</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Some authors suggest that the term BD does not have an adequate definition in the Medline &#40;MeSH&#41; thesaurus&#46; An in-depth review reveals that the term which best defines BD in healthcare publications is &#8220;volume&#8221;&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">7</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">It is clear that the field of medicine is dominated by a vast volume of healthcare data&#46; Such data include personal medical records&#44; medical images&#44; genetic data&#44; genomic sequences of population-based data&#44; clinical research data &#40;observational studies&#44; clinical trials&#44; etc&#46;&#41; and much more&#46; More recently&#44; this exponential growth has also been fueled by three-dimensional &#40;3D&#41; images&#44; as well as readings from biometric sensors or wearable devices&#44; i&#46;e&#46;&#44; devices incorporated into clothing or used bodily as implants or accessories which may act as an extension of the user&#39;s body or mind&#44; some them being widely used in endocrinology and clinical nutrition&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">While the concept of data volume in medicine&#44; as in other disciplines&#44; is relevant to BD&#44; the concept of BD is becoming increasingly associated with the five Vs&#58; volume&#44; velocity&#44; variety&#44; veracity and value&#46; We could say that we are talking about a data set or combinations of large bodies of data &#40;volume&#41;&#44; of a diverse and complex nature &#40;variety&#41;&#44; which grow rapidly and need to be processed &#40;velocity&#41; and to be real&#44; authentic and of quality &#40;veracity&#41;&#44; and which when appropriately analyzed afford value&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">Considering the above&#44; we cannot stay on the conceptual surface of the term&#44; and the great challenge&#44; in my opinion&#44; lies in the continuous development and real&#44; extensive and collaborative implementation of data analyzing systems that are more powerful than the traditional methods&#46; We must focus on developing platforms for the more effective capture&#44; storage and handling of these large volumes of data&#44; to allow us to add value to our healthcare activity&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">A good example of this is the Savana Manager tool&#44; used by Ballesteros et al&#46; in their study&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">8</span></a> This innovative system&#44; using EHRead technology&#44; is able to automatically analyze and extract the relevant clinical information contained in the free text of EHRs using natural language and BD processing techniques&#44; and to transform it into ordered information for research purposes&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">9</span></a></p><p id="par0050" class="elsevierStylePara elsevierViewall">In their study&#44; Ballesteros et al&#46; tested one of the BD utilities in clinical research and healthcare management&#46; The analysis of a large body of EHR data has shown that the underdiagnosis of disease-related malnutrition &#40;DRM&#41; remains a reality&#46; This circumstance is widely highlighted by different initiatives in the fight against malnutrition&#44; and is the constant concern of endocrinologists dedicated to clinical nutrition&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">10</span></a></p><p id="par0055" class="elsevierStylePara elsevierViewall">The BD of the study in question assessed more than 180&#44;000 hospitalization records and&#44; with less effort than that required in classical prevalence studies&#44; allowed for an assessment of the characteristics of the study population&#46; The patients identified as malnourished were mainly individuals with heart failure &#40;35&#37;&#41;&#44; respiratory infection &#40;23&#37;&#41;&#44; urinary infection &#40;20&#37;&#41; and chronic kidney disease &#40;15&#37;&#41;&#46; The study also established that these patients were older &#40;75 vs&#46; 59 years&#41;&#44; with greater mortality &#40;7&#46;08&#37; vs&#46; 2&#46;98&#37;&#41; and longer stay &#40;8 vs&#46; 5 days&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41; as compared to patients with no diagnosis of malnutrition&#46; The study established that 2&#46;47&#37; of the episodes included the diagnosis of DRM&#44; a figure far from the over 23&#37; reported in the PREDyCES study&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">11</span></a></p><p id="par0060" class="elsevierStylePara elsevierViewall">The true value of this exercise in BD analysis lies in the fact that&#44; with relatively less effort&#44; the authors have relevant information for managing the approach to DRM at their center&#46; With data showing under-diagnosis&#44; they are better able to adopt measures of improvement in care units&#46; This facilitates the early detection of malnourished patients or individuals at risk of malnutrition who can benefit from a specific intervention to both improve the quality of care and to lower the costs&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">We have sufficient evidence regarding how advances in BD and AI&#44; along with human intelligence&#44; lead to the practice of high-performance medicine&#46; The current applications of AI range from embryo selection in in vitro fertilization processes or medical monitoring using Alexa-type verbal language devices&#44; to mental health controls&#44; the monitoring of parameters of interest &#40;blood pressure&#44; heart rate&#44; electrocardiogram&#44; blood glucose&#44; etc&#46;&#41; or therapeutic adherence&#46; Other applications refer to paramedic interventions in the cardiological or neurological setting &#40;myocardial infarctions&#44; stroke&#44; etc&#46;&#41;&#44; aids for reading and interpreting radiological images&#44; the prevention of blindness &#40;retinography readings&#41;&#44; the identification of mutations causing cancer&#44; the promotion of patient safety&#44; and even the prediction of mortality in the hospital setting&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">12</span></a> It is to be expected that these advances will allow for further improvements in disease prevention&#44; diagnosis and treatment&#44; as well as better and more efficient healthcare management&#44; with a positive impact upon quality and efficiency&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">However&#44; in order to reach these goals capable of transporting the healthcare system to a new era&#44; we need to work together to implicate those involved in the development of the digitalization process &#40;public administration&#44; private companies&#44; hospitals&#44; physicians&#44; research centers&#44; universities&#44; etc&#46;&#41;&#46; One of the most important challenges in this development process in all countries is to integrate the technology with privacy and confidentiality policies&#44; infrastructures&#44; and a data sharing culture&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">13</span></a> Although we are aware that digital transformation in health in Spain has already begun&#44; authoritative voices have proposed for some years the development of a National Digital Health Strategy&#44; taking into account all of these factors&#44; incorporating a clear framework of cooperation&#44; prioritizing the implementation of shared value use cases&#44; and making it feasible to measure the resulting impact&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">14</span></a></p></span>"
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