{"paper_id":"4a37daac-9bf5-4f90-b6f6-0806f99b4fc2","body_text":":root { --font-inter: 'Inter', sans-serif, Symbol; --font-monospace: monospace; --font-sans-serif: sans-serif; } html, body, div, span, h1, h2, h3, h4, h5, h6, p, ol, ul, li, table, caption, tbody, tfoot, thead, tr, th, td {margin: 0;padding: 0;border: 0;font-size: 100%;font: inherit;vertical-align: baseline;} html {font-size: 14px;line-height: 1.5;} body {font-size: 14px;line-height: 1.5;font-family: var(--font-Inter);} ol, ul {list-style: none;} blockquote, q {quotes: none;} blockquote:before, blockquote:after, q:before, q:after {content: '';content: none;} img {max-width: 100%;} table {border-collapse: collapse;border-spacing: 0;} b {font-weight: bold;} a {color: #2c71d6;text-decoration: none;} p {margin: 10px 0 10px 0;} .no-select {-o-user-select: none;-moz-user-select: none;-khtml-user-select: none;-webkit-user-select: none;user-select: none;} * {box-sizing: border-box;} @keyframes blink {0% {opacity: .1;}20% {opacity: 1;}100% {opacity: .1;}} .main-preloader{position:fixed;display: flex;top:0;bottom:0;left:0;right:0;align-items: center;justify-content: center;z-index:200000000;text-align:center;background:#F2F3F6;} .main-preloader.dark{background:#363636;} .main-preloader.hidden {display: none;} .main-preloader .mp-logo{height:100px;width:100px;} Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Rec... {\"@context\":\"https://schema.org\",\"@id\":\"https://www.protocols.io/#org\",\"@type\":\"Organization\",\"logo\":{\"@type\":\"ImageObject\",\"height\":\"1550\",\"url\":\"https://www.protocols.io/img/protocols-og.jpg\",\"width\":\"1920\"},\"name\":\"protocols.io\",\"url\":\"https://www.protocols.io/\"} {\"@context\":\"https://schema.org\",\"@id\":\"https://www.protocols.io/#website\",\"@type\":\"WebSite\",\"name\":\"protocols.io\",\"publisher\":{\"@id\":\"https://www.protocols.io/#org\"},\"url\":\"https://www.protocols.io/\"} {\"@context\":\"https://schema.org\",\"@id\":\"https://www.protocols.io/view/developing-deep-learning-continuous-risk-models-fo-eq2ly6y3egx9/v1#webpage\",\"@type\":\"WebPage\",\"about\":{\"@id\":\"https://www.protocols.io/#org\"},\"description\":\"Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study\\n. . Read full protocol, st...\",\"isPartOf\":{\"@id\":\"https://www.protocols.io/#website\"},\"name\":\"Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Rec...\",\"url\":\"https://www.protocols.io/view/developing-deep-learning-continuous-risk-models-fo-eq2ly6y3egx9/v1\"} {\"@context\":\"https://schema.org\",\"@id\":\"https://www.protocols.io/view/developing-deep-learning-continuous-risk-models-fo-d3i38kgn#article\",\"@type\":\"Article\",\"author\":{\"@type\":\"Person\",\"affiliation\":\"Dev\",\"name\":\"Protocols Importer\"},\"dateModified\":\"2019-07-31T16:36:20Z\",\"datePublished\":\"2019-07-31T16:36:20Z\",\"description\":\"Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study\\n. . Read full protocol, st...\",\"headline\":\"Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Rec...\",\"image\":{\"@type\":\"ImageObject\",\"url\":\"https://files.protocols.io/webapp/images/q2ckeg/meta/protocolsio.png\"},\"mainEntityOfPage\":{\"@id\":\"https://www.protocols.io/view/developing-deep-learning-continuous-risk-models-fo-d3i38kgn#webpage\"},\"publisher\":{\"@id\":\"https://www.protocols.io/#org\"}} var WebappBundlePath = \"https:\\/\\/files.protocols.io\\/webapp\\/bundles\\/production-12becbb\\/\"; var AMPLITUDE_API_KEY=\"c250ed1d862cb9c01587e1f779a32c9\";var FFMPEG_HOST=\"https://ffmpeg.protocols.io\";var FILES_CDN_URL=\"https://files.protocols.io\";var FILES_S3_BUCKET=\"protocols-io-files\";var FILES_S3_PREFIX=\"external\";var GA_MEASUREMENT_ID=\"G-33ZGV5CT0B\";var GOOGLE_CLIENT_ID=\"182746271041-rdtjk0b1h9706r0fgpq1o2h55lgbo8un.apps.googleusercontent.com\";var HAS_SSR= false ;var HOST_ROLE= 1 ;var HOST_ROLES={\"HOST_DEV\":2,\"HOST_LOCAL\":3,\"HOST_PROD\":1};var IMAGE_BASE_PATH=\"https://files.protocols.io/webapp/images/q2ckeg/\";var NATS_SERVER=\"wss://ws.protocols.io\";var ORCID_CLIENT_ID=\"APP-7405TP4958GFSZXV\";var ORCID_URL=\"https://orcid.org/oauth/authorize\";var RECAPTCHA_SITE_ID=\"6LeMF6YUAAAAAOYzOqfc9Ml3PdTgIzO1oDdIwCYc\";var SITE_URL=\"https://www.protocols.io\";var SPRINGERNATURE_CLIENT_ID=\"protocolsio\";var SPRINGERNATURE_REDIRECT=\"https://www.protocols.io/api/v1/auth/springernature/authcallback\";var SPRINGERNATURE_URL=\"https://idp.springernature.com\";var STRIPE_PUB_KEY=\"pk_live_51PfMxdRshFFmaQRVGeDsSX7NFFLMkMuqr17SxP6JcJuieglmQYHPgM7cz0XdO91IEVP9k4E4MC7PZB2ATyuuaOy800ivYpRURi\";var UIPE=\"ccW9Kn73Dw3C4V1lovN9dhdEvizyoN5hpBOzwM24c7Y=\";var VPC_ALLOW_REGENTS_LINKING= false ;var VPC_DISABLED_INVITE_COLLEAGUES= false ;var VPC_DISABLED_PUBLISHING= false ;var VPC_DISABLED_SIGN_UP= false ;var VPC_DISABLED_WORKSPACE_CREATION= false ;var VPC_EXTERNAL_STORAGES= null ;var VPC_FORWARDED_VIEW_URL_PREFIX=\"\";var VPC_MAIN_CLOUD_URL=\"https://www.protocols.io\";var VPC_REQUIRE_SIGN_IN= false ;var VPC_USE_MAIN_GLOBAL_SEARCH= false ;var WEBAPP_VERSION=\"production-12becbb\";var YJS_WEBSOCKET_URL=\"wss://www.protocols.io\";var __stripeGlobal= null ;var cookie_session_guid=\"A4F7C6D287C111F183C60A58A9FEAC02\";var gosessid=\"A4F7C5DB87C111F183C60A58A9FEAC02\";var is_premium= false ;var loadedAmplitude= false ;var loadedGA= false ;var logged_in= false ;var session_guid=\"\";var show_loader= true ;var user_theme=\"default\"; var is_grecaptcha_loaded = false; if (!Element.prototype.matches) Element.prototype.matches = Element.prototype.msMatchesSelector; if (!Element.prototype.closest) Element.prototype.closest = function (selector) {var el = this; while (el) {if (el.matches(selector)) return el; el = el.parentElement;}};","source_license":"CC-BY-4.0","license_restricted":false}