<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1684-1859</journal-id>
<journal-title><![CDATA[Revista Cubana de Informática Médica]]></journal-title>
<abbrev-journal-title><![CDATA[RCIM]]></abbrev-journal-title>
<issn>1684-1859</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de La Habana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1684-18592019000100088</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[¿Por qué emplear el análisis estadístico implicativo en los estudios de causalidad en salud?]]></article-title>
<article-title xml:lang="en"><![CDATA[Why Use the Implicative Statistical Analysis in Studies of Health Causality?]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sagaró del Campo]]></surname>
<given-names><![CDATA[Nelsa María]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zamora Matamoros]]></surname>
<given-names><![CDATA[C. Larisa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Ciencias Médicas  ]]></institution>
<addr-line><![CDATA[Santiago de Cuba ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Oriente Santiago de Cuba ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>11</volume>
<numero>1</numero>
<fpage>88</fpage>
<lpage>103</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1684-18592019000100088&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1684-18592019000100088&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1684-18592019000100088&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El análisis estadístico implicativo es una técnica de minería de datos, surgida para resolver problemas de la didáctica de las matemáticas, se basa en la inteligencia artificial y el álgebra booleana, para modelar la casi implicación entre eventos y variables de un conjunto de datos. El objetivo de este ensayo es exponer las evidencias teóricas y prácticas que demuestran su utilidad para el estudio de la causalidad en la salud, para lo cual se realizó una revisión exhaustiva del tema en las bases de datos bibliográficas alojadas en Internet. Se presentan una serie de razones que justifican el uso de esta técnica en estudios de causalidad en medicina, en relación con el número de variables, el tamaño de la muestra, los supuestos requeridos para su aplicación y la naturaleza asimétrica de sus índices. También se identifican algunas ventajas con respecto a las técnicas estadísticas tradicionales, como la detección de eventos raros, que pasan inadvertidos a medidas como el apoyo y la confianza. Finalmente, se mencionan las investigaciones clínico-epidemiológicas donde se ha utilizado este análisis.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Implicative statistical analysis is a technique of data mining, emerged to solve problems of the Didactic of mathematics, it is based on Artificial Intelligence and Boolean Algebra, to model the quasi-implication between events and variables of a data set. The objective of this essay is to expose the theoretical and practical evidences that demonstrate its utility for the study of causality in health, for which an exhaustive review of the subject was carried out in the bibliographic databases hosted on the internet. A series of reasons are presented that justify the use of this technique in causality studies in medicine, regarding the number of variables, the sample size, the assumptions required for its application and the asymmetric nature of its indices. Also some advantages are identified with respect to traditional statistical techniques such as detection of rare events, which would go unnoticed to measures such as support and trust. Finally, clinical-epidemiological investigations where this analysis has been used are mentioned.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Análisis estadístico implicativo]]></kwd>
<kwd lng="es"><![CDATA[cuasi-implicación]]></kwd>
<kwd lng="es"><![CDATA[similaridad]]></kwd>
<kwd lng="es"><![CDATA[cohesión]]></kwd>
<kwd lng="es"><![CDATA[entropía]]></kwd>
<kwd lng="es"><![CDATA[causalidad en medicina]]></kwd>
<kwd lng="es"><![CDATA[técnicas estadísticas]]></kwd>
<kwd lng="en"><![CDATA[Implicative statistical analysis]]></kwd>
<kwd lng="en"><![CDATA[quasi-implication]]></kwd>
<kwd lng="en"><![CDATA[similarity]]></kwd>
<kwd lng="en"><![CDATA[cohesion]]></kwd>
<kwd lng="en"><![CDATA[entropy]]></kwd>
<kwd lng="en"><![CDATA[causality in medicine]]></kwd>
<kwd lng="en"><![CDATA[statistical techniques]]></kwd>
</kwd-group>
</article-meta>
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