{"id":9554,"date":"2014-03-26T13:59:49","date_gmt":"2014-03-26T12:59:49","guid":{"rendered":"http:\/\/catai.net\/blog\/?p=9554"},"modified":"2014-03-26T14:18:06","modified_gmt":"2014-03-26T13:18:06","slug":"big-data-definition-in-healthcare","status":"publish","type":"post","link":"https:\/\/catai.net\/blog\/2014\/03\/big-data-definition-in-healthcare\/","title":{"rendered":"BIG DATA definition in Healthcare."},"content":{"rendered":"

Data<\/strong> can be classified in 3 groups.<\/p>\n

    \n
  1. Structured<\/strong> data. ie. Any data structured in a data base<\/li>\n
  2. Semi-structured<\/strong> data. ie. Any data coming from an XML document, since it can be partially stored into a data base.<\/li>\n
  3. Unstructured<\/strong> data or BIG DATA<\/a>.\u00a0 Data from social media sites, web logs etc… that cannot be stored analysed and processed in databases.<\/li>\n<\/ol>\n

    This means that the idea that you can extract from millions of Clinical health records processed together as BIG DATA relevant information is basically wrong, since a CHR is main a structured or semi-structured data furfilling standards and specific vocabularies into XML structures.<\/p>\n

    And although it is true that part of the information could be unstructure and processed as BIG DATA with tools such as HADOOP ecosystem<\/a>, on the reality is that in most of the cases have to be processed as multimedia relational data bases.<\/p>\n

    \"\"<\/p>\n

    On this regard the essential tools of the big data<\/a> processing systems such as:<\/p>\n