{"id":10771,"date":"2016-06-14T20:10:22","date_gmt":"2016-06-14T19:10:22","guid":{"rendered":"http:\/\/catai.net\/blog\/?p=10771"},"modified":"2023-04-15T21:57:12","modified_gmt":"2023-04-15T20:57:12","slug":"health-4-0-and-industry-4-0","status":"publish","type":"post","link":"https:\/\/catai.net\/blog\/2016\/06\/health-4-0-and-industry-4-0\/","title":{"rendered":"Health 4.0 and Industry 4.0"},"content":{"rendered":"<div class=\"fit-vids-style\">\u00ad<\/div>\n<p><script type=\"text\/javascript\">\/\/ <![CDATA[\n(function() { var bsl=document.createElement('script'); bsl.type='text\/javascript';bsl.async=true;bsl.src='https:\/\/industrial-iot.com\/wp-content\/uploads\/it-file-cache\/builder-core\/script.js?ver=7'; var se=document.getElementsByTagName('script')[0];se.parentNode.insertBefore(bsl,se); })();<br \/>\n\/\/ ]]><\/script><script>\/\/ <![CDATA[\n(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','\/\/www.google-analytics.com\/analytics.js','ga'); ga('create', 'UA-63444333-1', 'auto'); ga('send', 'pageview');<br \/>\n\/\/ ]]><\/script><!-- Jetpack Open Graph Tags --><script src=\"https:\/\/analytics.clickdimensions.com\/tsr.js?ac=anPnvUQ0qh067EUvlbbbpw&amp;urk=1465924898520&amp;cm=&amp;s=1465924898491&amp;v=&amp;lc=English&amp;pt=Will%20Machine%20Learning%20Eat%20Historian%3F%20-%20Industrial%20IoT%2FIndustrie%204.0%20Viewpoints&amp;dn=industrial-iot.com&amp;dnk=&amp;pk=&amp;hn=industrial-iot.com&amp;uri=https%3A%2F%2Findustrial-iot.com%2F2016%2F06%2Fwill-machine-learning-eat-historian%2F&amp;r=https%3A%2F%2Fwww.linkedin.com%2Fhome%3Ftrk%3Dnav_responsive_tab_home&amp;t=PAGE&amp;b=CHROME&amp;os=WINDOWS&amp;fv=21.0.0&amp;ar=false&amp;pr=false&amp;sc=0&amp;sv=5.0.0&amp;qd=&amp;dt=1465924898520&amp;pvon=\" type=\"text\/javascript\"><\/script><script src=\"https:\/\/platform.twitter.com\/js\/button.177407e7c9fd9de66268350cb0968e03.js\" async=\"\" type=\"text\/javascript\" charset=\"utf-8\"><\/script><script src=\"https:\/\/platform.linkedin.com\/js\/secureAnonymousFramework?v=0.0.1191-RC8.56523-1429&amp;\"><\/script><\/p>\n<div class=\"builder-container-background-wrapper\">\n<div class=\"builder-container-outer-wrapper\">\n<div id=\"builder-container-554cc5af25f7d\" class=\"builder-container\">\n<div id=\"builder-module-554cc5af25ee3-background-wrapper\" class=\"builder-module-background-wrapper builder-module-content-background-wrapper builder-module-3-background-wrapper builder-module-content-1-background-wrapper builder-module-middle-background-wrapper builder-module-content-last-background-wrapper builder-module-before-widget-bar-background-wrapper builder-module-after-navigation-background-wrapper default-module-style-background-wrapper\">\n<div id=\"builder-module-554cc5af25ee3-outer-wrapper\" class=\"builder-module-outer-wrapper builder-module-content-outer-wrapper builder-module-3-outer-wrapper builder-module-content-1-outer-wrapper builder-module-middle-outer-wrapper builder-module-content-last-outer-wrapper builder-module-before-widget-bar-outer-wrapper builder-module-after-navigation-outer-wrapper default-module-style-outer-wrapper\">\n<div id=\"builder-module-554cc5af25ee3\" class=\"builder-module builder-module-content builder-module-3 builder-module-content-1 builder-module-middle builder-module-content-last builder-module-before-widget-bar builder-module-after-navigation default-module-style clearfix\">\n<div class=\"builder-module-block-outer-wrapper builder-module-element-outer-wrapper builder-module-column-1-outer-wrapper left clearfix\">\n<div class=\"builder-module-block builder-module-element builder-module-column-1 clearfix\">\n<div class=\"loop\">\n<div class=\"loop-content\">\n<div id=\"post-2953\" class=\"post-2953 post type-post status-publish format-standard hentry category-analytics-and-big-data category-platforms category-software tag-historian tag-machine-learning no-post-thumbnail\">\n<div class=\"entry-content clearfix\">\n<p>We have already introduce the differences between MACHINE LEARNING (ML) and <a href=\"https:\/\/catai.net\/blog\/2016\/02\/ml-vs-dl-machine-learning-versus-deep-learning-in-health-4-0\/\">DEEP LEARNING (DL)<\/a> in our previous post. &nbsp;The first require programming to encode the information the second is unsupervised.<\/p>\n<p><span style=\"text-decoration: underline;\">ML- MACHINE LEARNING<\/span> can identify normal conditions, abnormal conditions, and exactly match patterns that indicate degradation and impending process excursions or asset failure well before they happen.<\/p>\n<p style=\"padding-left: 60px;\">It can predict an accurate time-to-failure indicating precisely<\/p>\n<p style=\"padding-left: 90px;\"><strong>WHEN<\/strong> <strong><span style=\"color: #800000;\">a known failure<\/span><\/strong> will occur,<\/p>\n<p style=\"padding-left: 90px;\"><strong>HOW<\/strong> the failure will occur, and<\/p>\n<p style=\"padding-left: 90px;\"><strong>WHAT<\/strong> to do about it; derived from <strong><span style=\"text-decoration: underline;\"><span style=\"color: #800000; text-decoration: underline;\">Prescriptive advice<\/span><\/span><\/strong> such as the exact failure code directly linked from the EAM system. Knowing the precise, multiple days\u2019 or weeks\u2019 lead time to a failure allows the end user to determine the exact action necessary (often through discussions between Operations, Maintenance, Technical and Planning\/Scheduling Departments). Such Prescriptive action enables the best remediation and timing decisions to avoid damage altogether, prevent a breakdown, and solve the problem in the most efficient manner.<\/p>\n<p style=\"padding-left: 60px;\">In a CAR&nbsp;this is clear. The machine inform you 30 days&nbsp;in advance that&nbsp;something could happen in your engine and that you have to bring the car&nbsp;to a mechanic&nbsp;to get&nbsp;fixed.<\/p>\n<p style=\"padding-left: 60px;\">The HUMAN BODY is not so different. There are many parameters, indexes, predictors more or less upgraded that can tell you when something wrong might happens and that you need to be fixed before this occurs. This is implemented in Machine learning. It can take the advantage to get the information very early and therefore to prevent the problem if you are using wearables devices that measure permanently relevant data and evaluate existing EBM (evidence based medicine) PROGNOSTIC INDEXES. In other words, based on what happens with the average people that have been exposed to those conditions. In spite of the promissing results the published data up to now <a href=\"https:\/\/catai.net\/blog\/2016\/03\/learn-to-publish-telemedicine-results-is-essential\/\">have not been very possitive<\/a>. See <a href=\"https:\/\/catai.net\/blog\/2016\/05\/new-diy-analytics\/\">1<\/a>, <a href=\"https:\/\/catai.net\/blog\/2015\/06\/minimal-disruptive-medicine-mdm\/\">2<\/a>, <a href=\"https:\/\/catai.net\/blog\/2015\/06\/wireless-non-invasive-vs-cardiac-catheterism\/\">3<\/a>,<a href=\"https:\/\/catai.net\/blog\/2014\/12\/health-ai-thanks-to-wearables\/\"> 4<\/a>&nbsp;&amp; <a href=\"https:\/\/catai.net\/blog\/2015\/04\/basis-for-non-intrusive-wearables\/\">basis for non-intrusive wearables<\/a>.<\/p>\n<p><span style=\"text-decoration: underline;\">DL- DEEP LEARNING<\/span>, on the contrary is a step forward in the Health 4.0 environment (anytime-anymeans-anywhere-anyhow). It takes all kind of data relevant or not, from your body or from the environment or from the weather or from the news or from the medication or from the pollution or &nbsp;from the type of food you eat or what you drink etc&#8230; in order to be able to DISCOVER, &nbsp;in case&nbsp;something wrong is measured in your body, HOW the faillure occured&nbsp;to be prevented with&nbsp;time. DL is carrying for&nbsp;you an HOLISTIC MEDICINE,&nbsp;is putting into practice the 10 requirements for the <a href=\"https:\/\/catai.net\/blog\/2016\/03\/who-social-determinants-in-health\/\">REAL LIFE&nbsp;EVIDENCE (RLE) <\/a>&nbsp;of&nbsp;the social determinantes as the WHO stated. Data will be then <strong><a href=\"https:\/\/catai.net\/blog\/2015\/12\/data-analytics-in-healthcare\/\">Predictive- Personalize- Participative<\/a><\/strong>.<\/p>\n<p>Before we determine how to handle an holistic medicine with AI (artificial intelligence) techniques, both have to be in place when implementing IoT (internet of the things) for healthcare surveillance.<\/p>\n<p><a href=\"https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/06\/happiness.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-10779 size-full\" src=\"https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/06\/happiness.jpg\" alt=\"happiness\" width=\"500\" height=\"274\" srcset=\"https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/06\/happiness.jpg 500w, https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/06\/happiness-300x164.jpg 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><\/p>\n<p>See also&nbsp;<a href=\"https:\/\/catai.net\/blog\/2015\/12\/data-analytics-in-healthcare\/\">https:\/\/catai.net\/blog\/2015\/12\/data-analytics-in-healthcare\/<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u00ad We have already introduce the differences between MACHINE LEARNING (ML) and DEEP LEARNING (DL) in our previous post. &nbsp;The first require programming to encode the information the second is unsupervised. ML- MACHINE LEARNING can identify normal conditions, abnormal conditions, and exactly match patterns that indicate degradation and impending process excursions or asset failure well [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"give_campaign_id":0,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[43,40,13,28,29,32,9],"tags":[223,225,97,226,228,224,207,227,145,60],"class_list":["post-10771","post","type-post","status-publish","format-standard","hentry","category-health-4-0","category-innovacion","category-phealth","category-sanidad","category-seguridad","category-sociedad_red","category-unesco","tag-deep-learning","tag-holistic","tag-iot","tag-medicine","tag-non-intrusive","tag-real-life-evidence","tag-rle","tag-surveillance","tag-wearables","tag-who"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Health 4.0 and Industry 4.0 | Telemedicina<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/catai.net\/blog\/2016\/06\/health-4-0-and-industry-4-0\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Health 4.0 and Industry 4.0 | Telemedicina\" \/>\n<meta property=\"og:description\" content=\"\u00ad We have already introduce the differences between MACHINE LEARNING (ML) and DEEP LEARNING (DL) in our previous post. &nbsp;The first require programming to encode the information the second is unsupervised. 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