{"id":213,"date":"2021-02-16T15:41:15","date_gmt":"2021-02-16T14:41:15","guid":{"rendered":"https:\/\/informedica.nl\/?p=213"},"modified":"2021-02-26T08:42:30","modified_gmt":"2021-02-26T07:42:30","slug":"machine-learning-in-pediatric-critical-care","status":"publish","type":"post","link":"https:\/\/informedica.nl\/?p=213","title":{"rendered":"Machine Learning in Pediatric Critical Care"},"content":{"rendered":"\n<p>Machine Learning (ML) (or Artificial Intelligence AI) is trending. Publications with regard to ML are on the increase in medical literature.<\/p>\n\n\n\n<!DOCTYPE html>\n<html>\n    <head>\n        <!-- Plotly.js -->\n        <meta http-equiv=\"X-UA-Compatible\" content=\"IE=11\" >\n        <script src=\"https:\/\/cdn.plot.ly\/plotly-latest.min.js\"><\/script>\n        <style>\n        .container {\n          padding-right: 25px;\n          padding-left: 25px;\n          margin-right: 0 auto;\n          margin-left: 0 auto;\n        }\n        @media (min-width: 768px) {\n          .container {\n            width: 750px;\n          }\n        }\n        @media (min-width: 992px) {\n          .container {\n            width: 970px;\n          }\n        }\n        @media (min-width: 1200px) {\n          .container {\n            width: 1170px;\n          }\n        }\n        <\/style>\n    <\/head>\n    <body>\n      <div id=\"5927d584-b3c8-4ef8-9a08-c3d8a1e81d0a\" style=\"width: 500px; height: 600px;\"><!-- Plotly chart will be drawn inside this DIV --><\/div>\n<script type=\"text\/javascript\">\n\n            var renderPlotly_5927d584b3c84ef89a08c3d8a1e81d0a = function() {\n            var fsharpPlotlyRequire = requirejs.config({context:'fsharp-plotly',paths:{plotly:'https:\/\/cdn.plot.ly\/plotly-latest.min'}}) || require;\n            fsharpPlotlyRequire(['plotly'], function(Plotly) {\n\n            var data = [{\"type\":\"scatter\",\"x\":[2020,2019,2018,2017,2016,2015,2014,2013,2012,2011,2010,2009,2008,2007,2006,2005,2004,2003,2002],\"y\":[25.270089787498513,16.415640744218027,10.650199013527631,6.676540206021977,4.859632651442022,4.1825899527921395,3.7382806818031553,3.238432751940547,2.5137854885418456,2.5878370337066765,2.5428771669994576,2.8311492535339777,3.3957922854158125,3.0493368419660687,2.8060246221387675,2.509818441479444,1.5775623818150564,0.6413392750882668,0.5130714200706135],\"mode\":\"lines\",\"name\":\"All\",\"marker\":{}},{\"type\":\"scatter\",\"x\":[2020,2019,2018,2017,2016,2015,2014,2013,2012,2011,2010,2009,2008,2007,2006,2005,2004,2003,2002],\"y\":[42.5061425061425,24.57002457002457,14.127764127764127,5.036855036855036,3.6855036855036856,1.597051597051597,1.4742014742014742,0.7371007371007371,0.9828009828009828,0.7371007371007371,0.36855036855036855,0.4914004914004914,0.85995085995086,0.85995085995086,0.6142506142506142,0.36855036855036855,0.36855036855036855,0.2457002457002457,0.36855036855036855],\"mode\":\"lines\",\"name\":\"ICU\",\"marker\":{}},{\"type\":\"scatter\",\"x\":[2020,2019,2018,2017,2016,2015,2014,2012,2007],\"y\":[37.254901960784316,27.45098039215686,21.568627450980394,5.882352941176471,0.9803921568627451,1.9607843137254901,1.9607843137254901,0.9803921568627451,1.9607843137254901],\"mode\":\"lines\",\"name\":\"PICU\",\"marker\":{}}];\n            var layout = {\"title\":\"Relative increase in publications\"};\n            var config = {};\n            Plotly.newPlot('5927d584-b3c8-4ef8-9a08-c3d8a1e81d0a', data, layout, config);\n});\n            };\n            if ((typeof(requirejs) !==  typeof(Function)) || (typeof(requirejs.config) !== typeof(Function))) {\n                var script = document.createElement(\"script\");\n                script.setAttribute(\"src\", \"https:\/\/cdnjs.cloudflare.com\/ajax\/libs\/require.js\/2.3.6\/require.min.js\");\n                script.onload = function(){\n                    renderPlotly_5927d584b3c84ef89a08c3d8a1e81d0a();\n                };\n                document.getElementsByTagName(\"head\")[0].appendChild(script);\n            }\n            else {\n                renderPlotly_5927d584b3c84ef89a08c3d8a1e81d0a();\n            }\n<\/script>\n\n      \n    <\/body>\n<\/html>\n\n\n\n<p>Specifically critical care medicine generates huge amounts of detailed data. In a recent article steps are described to enable use of ML in daily clinical practice. This blog will describes an <strong>actual working<\/strong> implementation of the first step in ML to be used in clinical practice.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"846\" height=\"308\" src=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Machine-Learning-Steps.jpg\" alt=\"\" class=\"wp-image-214\" srcset=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Machine-Learning-Steps.jpg 846w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Machine-Learning-Steps-300x109.jpg 300w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Machine-Learning-Steps-768x280.jpg 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption><br>Mamdani, M., &amp; Slutsky, A. S. (2020). Artificial intelligence in intensive care medicine. <em>Intensive Care Medicine<\/em>, <em>47<\/em>(2), 147\u2013149. http:\/\/doi.org\/10.1007\/s00134-020-06203-2<\/figcaption><\/figure>\n\n\n<p><!--more--><\/p>\n\n\n<h4 class=\"wp-block-heading\">Quantity<\/h4>\n\n\n\n<p>Machine learning demands data, lots of data. When talking about ML, you&#8217;ll quickly discuss what is coined as &#8220;Big Data&#8221;. Essentially, whenever data processing is transformed from paper based to electronically stored, &#8220;Big Data&#8221; is generated. At our Pediatric Intensive Care Unit (PICU), along with our other Hospital&#8217;s Neonatal Care Unit (NICU) and adult ICU, this started at:<\/p>\n\n\n\n<p> <\/p>\n\n\n\n<pre title=\"Getting the start of data collection for PICURED\" class=\"wp-block-code\"><code lang=\"sql\" class=\"language-sql\"> -- Query to select the first data row\n -- with patient data stored in PICURED\n \n SELECT TOP 1 d.pat_datetime FROM dbo.Data d\n ORDER BY d.pat_datetime \n \n -- Query to the total amount of records\n SELECT COUNT(*) FROM dbo.Data d <\/code><\/pre>\n\n\n\n<p>Yielding <code>2008-03-24 13:30:00.000<\/code> as the start date of data collection for our PICURED PICU Research Database. From that moment on we have all patient related data at a minute to minute basis to our disposal. <\/p>\n\n\n\n<p>The subsequent query resulted at the time of writing in a staggering count of <code>100907433<\/code> records, i.e. more than 100 million records. So, there is quantity.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Availability<\/h4>\n\n\n\n<p>In order to apply ML to data, the data has to be in the right format. <\/p>\n\n\n\n<p>In ML world you have Features and Labels. What these are is briefly explained in this  <a rel=\"noreferrer noopener\" href=\"https:\/\/stackoverflow.com\/questions\/40898019\/what-is-the-difference-between-a-feature-and-a-label\" target=\"_blank\">StackOverflow answer<\/a>.<\/p>\n\n\n\n<p>The fact is, ML is closely related to some core concepts used in traditional epidemiology. Basically, epidemiology studies the Association between Exposure (and\/or Confounders) and Outcome.<\/p>\n\n\n\n<p>Translated to ML this corresponds with Features and Labels. Features are the quantitative equivalent of Exposure (or Confounders) and Labels can be mapped to Outcome. <\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Mapping-Exposure-Outcome-to-ML.jpg\" alt=\"\" class=\"wp-image-219\" width=\"674\" height=\"505\" srcset=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Mapping-Exposure-Outcome-to-ML.jpg 960w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Mapping-Exposure-Outcome-to-ML-300x225.jpg 300w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Mapping-Exposure-Outcome-to-ML-768x576.jpg 768w\" sizes=\"auto, (max-width: 674px) 100vw, 674px\" \/><figcaption>Mapping the Epidemiological concepts of Exposure, Confounder and Outcome to Rows of Features and Labels<\/figcaption><\/figure>\n\n\n\n<p>For ML to be able to compute a model, data has to be arranged in rows with the columns consisting of features and labels. So, essentially all data should be represented in a single (very large) table.  <\/p>\n\n\n\n<p>A Clinical hospital system or Patient Data Management systems (PDMS), does not store data in that format. A PDMS is designed to quickly write and read data for a single patient. In contrast, a data source for ML is essentially one big table containing all data for all patients.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Storage and Processing<\/h4>\n\n\n\n<p>Thus, data from a PDMS has to be processed, transformed and then stored, this can also be described as Extract, Transform and Loading (ETL) of data from a PDMS database to a research database.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"700\" height=\"247\" src=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/ETL-Process.jpg\" alt=\"\" class=\"wp-image-232\" srcset=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/ETL-Process.jpg 700w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/ETL-Process-300x106.jpg 300w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><figcaption>https:\/\/www.element61.be\/nl\/resource\/etl-extraction-transformation-loading<\/figcaption><\/figure>\n\n\n\n<p>In the specific case of PICURED, there are 2 source systems:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>The MetaVision PDMS database and<\/li><li>The Pedicatric Intensive Care Evaluation <a rel=\"noreferrer noopener\" href=\"https:\/\/pice.nl\" target=\"_blank\">PICE<\/a><\/li><\/ol>\n\n\n\n<p><a rel=\"noreferrer noopener\" href=\"https:\/\/www.imd-soft.com\/\" target=\"_blank\">MetaVision<\/a> is the PDMS in use in our Hospital on all intensive care units since 2008. PICE is a Dutch nationwide PICU registry in which data is collected to calculated mortality scores like PIM an PRISM and other features are collected like diagnoses. Taken together, our PDMS and PICE offer a rich source of data. However this data has to be transformed to columns in a single table. Therefore, a data model was created that looks like:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"957\" height=\"736\" src=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/PICURED-ETL.jpg\" alt=\"\" class=\"wp-image-237\" srcset=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/PICURED-ETL.jpg 957w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/PICURED-ETL-300x231.jpg 300w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/PICURED-ETL-768x591.jpg 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption>Extract Transform Load Clinical Data to PICURED<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>Raw data is modeled as <code>Signals<\/code> where each <code>Signal<\/code> is a single data point for a patient in time with a value.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"fsharp\" class=\"language-fsharp\">type SignalId = int\n\ntype Value =\n    | NoValue\n    | Text of string\n    | Numeric of float\n    | DateTime of DateTime\ntype Signal = \n    {\n        Id: SignalId\n        Name : string\n        Value : Value\n        PatientId : string\n        DateTime : DateTime option\n    }<\/code><\/pre>\n\n\n\n<p>These <code>Signals<\/code> are processed to <code>Observations<\/code>. An <code>Observation<\/code> is based on one ore more <code>Sources<\/code>, where a <code>Source<\/code> is converted <code>Value<\/code>. <code>Sources<\/code> are combined to a single <code>Observation<\/code> using a <code>Collapse<\/code> function.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"fsharp\" class=\"language-fsharp\">type Observation =\n     { Name : string \n       Type : string\n       Sources : Source list\n       Collapse : Collapse }\nand Source =\n     { Id : SignalId\n       Convert : Convert }<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"974\" height=\"770\" src=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-to-Observations.jpg\" alt=\"\" class=\"wp-image-241\" srcset=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-to-Observations.jpg 974w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-to-Observations-300x237.jpg 300w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-to-Observations-768x607.jpg 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption>Convert Signals to Observations<\/figcaption><\/figure>\n\n\n\n<p>The general algorithm uses a list of patient <code>Signals<\/code> (i.e. raw data), that is grouped by date time. So, for each date time there is a list of <code>Signals<\/code>, that occurred at that date time. Each date time list is processed using a list of <code>Observations<\/code>, such that a data row is created, identified by patient and date time, with <code>Observations<\/code> as columns.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"755\" src=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-Algorithm-1024x755.jpg\" alt=\"\" class=\"wp-image-242\" srcset=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-Algorithm-1024x755.jpg 1024w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-Algorithm-300x221.jpg 300w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-Algorithm-768x566.jpg 768w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/Signals-Algorithm.jpg 1188w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption>Signals to Observations Algorithm<\/figcaption><\/figure>\n\n\n\n<p>The algorithm loops through a list of <code>Observations<\/code>. For a single <code>Observation<\/code> the list of <code>Signals<\/code> is filtered looking for <code>Signals<\/code> that serve as <code>Source<\/code> for the <code>Observation<\/code>. The <code>Convert<\/code> function is used to convert the <code>Signal Value<\/code>. These converted <code>Values<\/code> are then collapsed (using the <code>Collapse<\/code> function) to a single <code>Observation Value<\/code>. <\/p>\n\n\n\n<p>Finally, the end result is a <code>DataSet<\/code>, that looks like:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"fsharp\" class=\"language-fsharp\">\/\/\/ The resulting dataset with colums\n\/\/\/ and rows of data. Each row has a\n\/\/\/ unique hospital number, date time.\ntype DataSet =\n    { Columns : Column list\n      Data : (PatientId * DateTime * DataRow) list }\nand Column = \n    { \n        Name: string\n        Type: string\n    }\nand DataRow = Value list<\/code><\/pre>\n\n\n\n<p>A working prototype can be found in this <a rel=\"noreferrer noopener\" href=\"https:\/\/gist.github.com\/halcwb\/7348669ddcfe942a6f0149be739a55e4\" target=\"_blank\">gist<\/a>. You can run the code in <a href=\"https:\/\/repl.it\/@halcwb\/HotGoodApplications#main.fs\" target=\"_blank\" rel=\"noreferrer noopener\">Repl.it<\/a>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Quality<\/h4>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"369\" height=\"635\" src=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/garbage_math.png\" alt=\"\" class=\"wp-image-252\" srcset=\"https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/garbage_math.png 369w, https:\/\/informedica.nl\/wp-content\/uploads\/2021\/02\/garbage_math-174x300.png 174w\" sizes=\"auto, (max-width: 369px) 100vw, 369px\" \/><figcaption>If there is Garbage, there is Garbage (<a href=\"https:\/\/xkcd.com\/2295\/\">https:\/\/xkcd.com\/2295\/<\/a>)<\/figcaption><\/figure>\n\n\n\n<p>The single most important problem in statistics, therefore also in Machine Learning, is the old adage:  <em>Garbage In, Garbage Out<\/em>.<\/p>\n\n\n\n<p>In the <code>Signal<\/code> processing algorithm, the <code>Convert<\/code> and <code>Collapse<\/code> functions can also be used to filter out non valid data.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"fsharp\" class=\"language-fsharp\">\ntype Convert = Signal -&gt; Signal\nlet filter pred : Convert = \n     fun signal -&gt;\n         let value =\n             signal\n             |&gt; Signal.getNumericValue\n             |&gt; Option.bind (fun value -&gt; \n                 \/\/ check the boundaries\n                 if value |&gt; pred  then NoValue\n                 else value |&gt; Numeric \n                 |&gt; Some\n             )\n             |&gt; Option.defaultValue NoValue\n         { signal with Value = value }\nlet filterLow low : Convert = \n     filter (fun value -&gt; value &lt; low)\nlet filterHigh high : Convert = \n     filter (fun value -&gt; value &gt; high)\nlet filterLowHigh low high : Convert = \n     (filterLow low) &gt;&gt; (filterHigh high)<\/code><\/pre>\n\n\n\n<p>Also, after processing can be applied by smoothing a time series.<\/p>\n\n\n\n\n<!DOCTYPE html>\n<html>\n    <head>\n        <!-- Plotly.js -->\n        <meta http-equiv=\"X-UA-Compatible\" content=\"IE=11\" >\n        <script src=\"https:\/\/cdn.plot.ly\/plotly-latest.min.js\"><\/script>\n        <style>\n        .container {\n          padding-right: 25px;\n          padding-left: 25px;\n          margin-right: 0 auto;\n          margin-left: 0 auto;\n        }\n        @media (min-width: 768px) {\n          .container {\n            width: 750px;\n          }\n        }\n        @media (min-width: 992px) {\n          .container {\n            width: 970px;\n          }\n        }\n        @media (min-width: 1200px) {\n          .container {\n            width: 1170px;\n          }\n        }\n        <\/style>\n    <\/head>\n    <body>\n      <div id=\"e75ba77a-210a-4629-b2ee-2a6d0d022e63\" style=\"width: 1000px; height: 800px;\"><!-- Plotly chart will 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           var layout = {\"title\":\"Mean Invasive Blood Pressure\",\"width\":1000.0,\"height\":800.0};\n            var config = {};\n            Plotly.newPlot('e75ba77a-210a-4629-b2ee-2a6d0d022e63', data, layout, config);\n});\n            };\n            if ((typeof(requirejs) !==  typeof(Function)) || (typeof(requirejs.config) !== typeof(Function))) {\n                var script = document.createElement(\"script\");\n                script.setAttribute(\"src\", \"https:\/\/cdnjs.cloudflare.com\/ajax\/libs\/require.js\/2.3.6\/require.min.js\");\n                script.onload = function(){\n                    renderPlotly_e75ba77a210a4629b2ee2a6d0d022e63();\n                };\n                document.getElementsByTagName(\"head\")[0].appendChild(script);\n            }\n            else {\n                renderPlotly_e75ba77a210a4629b2ee2a6d0d022e63();\n            }\n<\/script>\n\n      \n    <\/body>\n<\/html>\n\n\n\n<p>The above graph shows actual patient data of mean arterial blood pressures. By applying a smoothing algorithm, outliers can be filtered out.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Conclusions<\/h4>\n\n\n\n<p>In this blog a real life working framework is described that enables production data to be analysed by machine learning. The framework is generic and can be used in a variety of settings for a variety of machine learning projects. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine Learning (ML) (or Artificial Intelligence AI) is trending. Publications with regard to ML are on the increase in medical literature. Specifically critical care medicine generates huge amounts of detailed data. In a recent article steps are described to enable use of ML in daily clinical practice. This blog will describes an actual working implementation &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/informedica.nl\/?p=213\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Machine Learning in Pediatric Critical Care&#8221;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-213","post","type-post","status-publish","format-standard","hentry","category-general"],"_links":{"self":[{"href":"https:\/\/informedica.nl\/index.php?rest_route=\/wp\/v2\/posts\/213","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/informedica.nl\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/informedica.nl\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/informedica.nl\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/informedica.nl\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=213"}],"version-history":[{"count":24,"href":"https:\/\/informedica.nl\/index.php?rest_route=\/wp\/v2\/posts\/213\/revisions"}],"predecessor-version":[{"id":264,"href":"https:\/\/informedica.nl\/index.php?rest_route=\/wp\/v2\/posts\/213\/revisions\/264"}],"wp:attachment":[{"href":"https:\/\/informedica.nl\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=213"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/informedica.nl\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=213"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/informedica.nl\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=213"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}