{"id":2869,"date":"2020-09-24T21:44:36","date_gmt":"2020-09-24T21:44:36","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/09\/24\/researchers-use-artificial-intelligence-tools-to-predict-loneliness\/"},"modified":"2020-09-24T21:44:36","modified_gmt":"2020-09-24T21:44:36","slug":"researchers-use-artificial-intelligence-tools-to-predict-loneliness","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/09\/24\/researchers-use-artificial-intelligence-tools-to-predict-loneliness\/","title":{"rendered":"Researchers use artificial intelligence tools to predict loneliness"},"content":{"rendered":"

Researchers use artificial intelligence tools to predict loneliness<\/a><\/p>\n

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\n \"loneliness\"
\n Credit: CC0 Public Domain
\n <\/figcaption><\/figure>\n<\/p><\/div>\n<\/div>\n

For the past couple of decades, there has been a loneliness pandemic, marked by rising rates of suicides and opioid use, lost productivity, increased health care costs and rising mortality. The COVID-19 pandemic, with its associated social distancing and lockdowns, have only made things worse, say experts.\n <\/p>\n

Accurately assessing the breadth and depth of societal loneliness is daunting, limited by available tools, such as self-reports. In a new proof-of-concept paper, published online September 24, 2020 in the American Journal of Geriatric Psychiatry<\/i>, a team led by researchers at University of California San Diego School of Medicine used artificial intelligence technologies to analyze natural language<\/a> patterns (NLP) to discern degrees of loneliness in older adults.<\/p>\n

“Most studies use either a direct question of ‘ how often do you feel lonely,’ which can lead to biased responses due to stigma associated with loneliness or the UCLA Loneliness Scale which does not explicitly use the word ‘lonely,'” said senior author Ellen Lee, MD, assistant professor of psychiatry at UC San Diego School of Medicine. “For this project, we used natural language processing or NLP, an unbiased quantitative assessment of expressed emotion and sentiment, in concert with the usual loneliness measurement tools.”<\/p>\n

In recent years, numerous studies have documented rising rates of loneliness in various populations of people, particularly those most vulnerable, such as older adults. For example, a UC San Diego study published earlier this year found that 85 percent of residents living in an independent senior housing community reported moderate to severe levels of loneliness.<\/p>\n

The new study also focused on independent senior living residents: 80 participants aged 66 to 94, with a mean age of 83 years. But, rather than simply asking and documenting answers to questions from the UCLA Loneliness Scale, participants were also interviewed by trained study staff in more unstructured conversations that were analyzed using NLP-understanding software developed by IBM, plus other machine-learning tools.<\/p>\n

“NLP and machine learning allow us to systematically examine long interviews from many individuals and explore how subtle speech features like emotions may indicate loneliness. Similar emotion analyses by humans would be open to bias, lack consistency, and require extensive training to standardize,” said first author Varsha Badal, Ph.D., a postdoctoral research fellow.<\/p>\n

Among the findings:<\/p>\n

    \n
  • Lonely individuals had longer responses in qualitative interview, and more greatly expressed sadness to direct questions about loneliness.<\/li>\n
  • Women were more likely than men to acknowledge feeling lonely during interviews.<\/li>\n
  • Men used more fearful and joyful words in their responses compared to women. <\/li>\n<\/ul>\n

    Authors said the study highlights the discrepancies between research assessments for loneliness and an individual’s subjective experience of loneliness, which NLP-based tools could help to reconcile. The early findings reflect how there may be “lonely speech” that could be used to detect loneliness in older adults, improving how clinicians and families assess and treat loneliness in older adults, especially during times of physical distancing and social isolation.<\/p>\n

    The study, said the authors, demonstrates the feasibility of using natural language pattern analyses of transcribed speech to better parse and understand complex emotions like loneliness. They said the machine-learning models predicted qualitative loneliness with 94 percent accuracy.<\/p>\n

    “Our IBM-UC San Diego Center is now exploring NLP signatures of loneliness<\/a> and wisdom, which are inversely linked in older adults<\/a>. Speech data can be combined with our other assessments of cognition, mobility, sleep, physical activity and mental health to improve our understanding of aging and to help promote successful aging” said study co-author Dilip Jeste, MD, senior associate dean for healthy aging and senior care and co-director of the IBM-UC San Diego Center for Artificial Intelligence for Healthy Living.\n <\/p>\n

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    \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t<\/svg><\/p>\n

    Explore further<\/p>\n<\/p><\/div>\n

    \t\t\t\t\t\t\t\t\t\t\t Loneliness levels high during COVID-19 lockdown<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n

    \n
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tMore information:<\/strong>
    \n\t\t\t\t\t\t\t\t\t\t\t\tVarsha D. Badal et al, Prediction of Loneliness in Older Adults using Natural Language Processing: Exploring Sex Differences in Speech, The American Journal of Geriatric Psychiatry<\/i> (2020).     DOI: 10.1016\/j.jagp.2020.09.009<\/a><\/p><\/div>\n
    \n\t\t\t\t\t\t\t\t\t\t\t\tProvided by
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t    University of California – San Diego<\/a>
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/svg>
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a><\/p><\/div>\n

    <\/p>\n

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    \n Citation<\/strong>:
    \n Researchers use artificial intelligence tools to predict loneliness (2020, September 24)
    \n retrieved 24 September 2020
    \n from https:\/\/medicalxpress.com\/news\/2020-09-artificial-intelligence-tools-loneliness.html\n <\/p>\n

    \n This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
    \n part may be reproduced without the written permission. The content is provided for information purposes only.\n <\/p>\n<\/p><\/div>\n<\/p>\n

    Published at Thu, 24 Sep 2020 19:30:00 +0000<\/p>\n

    SciBite introduces insight-building AI software<\/a><\/p>\n

    SciBite introduces insight-building AI software<\/title><meta name=\"copyright\" content=\"William Reed Business Media\"><meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\"><meta name=\"format-detection\" content=\"telephone=no\"><meta http-equiv=\"Cache-Control\" content=\"public\"><meta name=\"google-site-verification\" content=\"ICOV4-XsW49rsFjskID7hXZPWSxuDrS9pzIo_2JNBBk\"><meta property=\"fb:admins\" content=\"100000722238608\"><meta property=\"fb:page_id\" content=\"415753095141636\"><meta property=\"twitter:site\" content=\"@OutsourcPharma\"><meta property=\"twitter:domain\" content=\"www.outsourcing-pharma.com\"><meta property=\"og:site_name\" content=\"outsourcing-pharma.com\"><meta property=\"author\" content=\"outsourcing-pharma.com\"><meta property=\"msvalidate.01\" content=\"CEC8ACB0DB9777352167906B78F1EF96\"><meta property=\"verify-v1\" content=\"WHomov305bLuKa7U4ALUYG\/IcW\/V1\/nBXyagl4I9gR0=\"><link rel=\"Shortcut icon\" 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insight-building AI software\"><meta property=\"og:description\" content=\"The AI platform combines machine-learning models and advanced semantic technologies to help life-science users gain insights from their data.\"><meta property=\"og:type\" content=\"article\"><meta property=\"og:url\" content=\"https:\/\/www.outsourcing-pharma.com\/Article\/2020\/09\/24\/SciBite-introduces-insight-building-AI-software\"><meta name=\"description\" content=\"The AI platform combines machine-learning models and advanced semantic technologies to help life-science users gain insights from their data.\"><meta name=\"keywords\" content=\"Clinical Development, Clinical evolution, Data management, Phase I-II, Phase III-IV, Preclinical, Artificial intelligence, machine learning, Data management, data analysis, Drug development\"><meta name=\"news_keywords\" content=\"Artificial intelligence, machine learning, Data management, data analysis, Drug development\"><link rel=\"canonical\" href=\"https:\/\/www.outsourcing-pharma.com\/Article\/2020\/09\/24\/SciBite-introduces-insight-building-AI-software\"><link rel=\"stylesheet\" href=\"https:\/\/fonts.googleapis.com\/css?family=Open+Sans:400,400italic,600,700\"><link rel=\"stylesheet\" href=\"https:\/\/cdn-a.william-reed.com\/css\/9fd7a26.css?1600938462\"><link rel=\"stylesheet\" href=\"https:\/\/cdn-a.william-reed.com\/css\/52bd55a.css?1600938462\"><!--[if IE 9]><script>document.documentElement.className+=' IE9';<\/script><![endif]--><noscript><\/p>\n<p><\/noscript><\/p>\n<div class=\"wrapper\">\n<div class=\"BodyLayout\" data-is-mobile=\"0\">\n<p> <main class=\"Layout\" role=\"main\"><\/p>\n<div class=\"Layout-main\">\n<article class=\"Detail\" data-widget=\"CopyAlert-dyn\" data-news-id=\"3329574\">\n<aside class=\"Ad positionLeft rwd--noMobile adBox\"><\/aside>\n<div class=\"Detail-content RichText\" readability=\"46.5\">\n<div class=\"ezxmltext-field RichText\" readability=\"63\">\n<p>SciBite\u2019s artificial intelligence (AI) software platform is designed to help pharmaceutical researchers and other life-science professionals parse through their data to unlock useful insights. According to the company, the platform pairs machine learning with ontology-based semantic capabilities.<\/p>\n<p>James Malone (JM), SciBite\u2019s chief technology officer, spoke with Outsourcing-Pharma about the progress of AI use and understanding in the pharma industry, and how the company\u2019s AI technology seeks to build upon previous technological capabilities.<\/p>\n<p><strong>OSP: Please talk a bit about the evolution of AI\u2019s use in life sciences\u2014how long it\u2019s been present, how its understanding and application has changed in the industry, and what might lie ahead?<\/strong>\u200b<\/p>\n<p>JM: There is a broad spectrum of approaches in AI, some of which have been used for a long time in life sciences. For instance, knowledge engineering using ontologies to describe metadata, expert systems for helping triage symptoms online, and machine learning for image analysis.<\/p>\n<p>Most recently the innovation in deep learning, combined with availability of big data and powerful compute, has provided huge improvements in the performance of some of these approaches. This is particularly true of areas such as language comprehension where they now represent the state of the art. It is likely these approaches will be increasingly combined into software in the near future and that scientists will benefit from the innovation without having to become deep learning experts.<\/p>\n<\/div>\n<aside id=\"text-ad1\" class=\"AlertAd-front\"><\/aside>\n<p><!-- \/excluded --><\/div>\n<\/article>\n<\/div>\n<p><\/main><\/div>\n<\/div>\n<p>In some domains, the future is already here, with voice recognition software commonplace in many applications. In the area of semantics, this may include approaches in harmonizing electronic medical records, analyzing self-reported patient data, and enabling natural language questions to be asked of large data stores.<\/p>\n<p><strong>OSP: What are some of the challenges the industry has faced that SciBiteAI is designed to help overcome?<\/strong>\u200b<\/p>\n<p>JM: The primary goals of SciBiteAI are to combine our expertise in semantics and life sciences to enable a broad as possible audience to benefit from machine learning approaches we are offering. One of the biggest barriers to building machine learning models is obtaining high-quality training data.<\/p>\n<p>Our existing technology means we are able to identify and create relevant training sets in an efficient and accurate manner. Our understanding of biomedical entities – drugs, diseases, genes, assays, etc. – are encoded in our ontologies and in turn are built into the machine learning models we derive from them.<\/p>\n<p>We are building our life sciences understanding into SciBiteAI – this is what we call semantics-based deep learning. A huge amount of human understanding is already encoded in a computer-readable form via ontologies. However, many AI companies don\u2019t utilize this resource and are in essence using AI with \u201cone hand tied behind its back\u201d; we\u2019re asking AI to make predictions and it\u2019s much better to arm it with what we know already rather than ask it to work \u201cin the dark\u201d.<\/p>\n<p>This combined strategy has been shown many times to outperform a single approach and is perhaps most famously demonstrated as the strategy that Watson used to win Jeopardy. Given SciBite\u2019s vast ontology resources, we can create workflows that comprehend scientific data and extract entities and patterns pertinent to those working in the field with many possible applications, for instance drug-adverse event detection, finding biomarkers or identifying novel biologics in text.<\/p>\n<p><strong>OSP: Why is incorporating AI technology that does not require becoming an AI wizard beneficial to life-science users?<\/strong>\u200b<\/p>\n<div class=\"object-left\">\n<figure class=\"hasCaption\"><a href=\"https:\/\/i0.wp.com\/cdn-a.william-reed.com\/var\/wrbm_gb_food_pharma\/storage\/images\/media\/images\/osp_scibiteai_jm\/11795867-1-eng-GB\/OSP_SciBiteAI_JM.jpg?ssl=1\" data-widget=\"LightboxGallery\"><img data-recalc-dims=\"1\" decoding=\"async\" data-src=\"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/09\/B5g60m.jpg?w=640&ssl=1\" alt=\"OSP_SciBiteAI_JM\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\"><\/a><figcaption><?xml version=\"1.0\" encoding=\"utf-8\"??????><\/p>\n<section xmlns:image=\"http:\/\/ez.no\/namespaces\/ezpublish3\/image\/\" xmlns:xhtml=\"http:\/\/ez.no\/namespaces\/ezpublish3\/xhtml\/\" xmlns:custom=\"http:\/\/ez.no\/namespaces\/ezpublish3\/custom\/\"><paragraph xmlns:tmp=\"http:\/\/ez.no\/namespaces\/ezpublish3\/temporary\/\">James Malone, chief technology officer, SciBite<\/paragraph><\/section>\n<\/figcaption><\/figure>\n<\/div>\n<p>JM: The ethos of SciBite is to enable the widest audience possible to benefit from advances in semantic technology. Tools such as TERMite for advanced named entity recognition and CENtree for democratizing enterprise ontology management, have brought technology often seen as the domain of experts, to a large audience of scientists, researchers, and application developers; SciBiteAI follows this same pattern, enabling simple calls to the tool which can exploit a lot of powerful deep learning underneath the hood.<\/p>\n<p>The alternative, of data collection, building training sets, creating code to train models and tweak them, then to wrap that up for use by others, consistently, can represent a significant time investment and a barrier for many. Data is an incredibly important asset for everyone working in the life science field, from big pharma to clinics to academic groups. Maximizing the value of this data to anyone using it is our mission.<\/p>\n<p>Another aspect of this is systems integration and in particular \u201cproductionizing\u201d AI. Many AI models are developed to address specific questions raised in a particular experiment or study. As such there is less attention paid to how such models are deployed or re-used within other applications.<\/p>\n<p>Our ultimate goal is to have services based on AI that are integrated into day-to-day scientific applications – indeed the user may not even know there is an AI-based algorithm operating. All they get is a system that does what they expect. For instance, smart data-entry systems that understand what users are entering into form fields and modify the forms behavior based on constant assessment of what the user is trying to do.<\/p>\n<p><strong>OSP: Can you share any examples of the SciBiteAI technology being put to use in a real-world situation?<\/strong>\u200b<\/p>\n<p>JM: We are about to publish a study on the use of the technology in accurately identifying novel interactions between biological molecules, distinguishing between inconsequential mentions (e.g. two molecules mentioned in a list) from significant events (\u2018X\u2019 activates \u2018Y\u2019 for example). This is a key part of generating computable knowledge and a hard problem for the field, but these advances will lead to better accuracy of extracted facts and consequently better insights and productivity.<\/p>\n<p><strong>OSP: What would you like to add about the technology that I didn\u2019t touch upon above?<\/strong>\u200b<\/p>\n<p>JM: As with any technology area that makes rapid advances, there is much excitement, a degree of hype and a lot of potential. Our approach to utilizing these innovations, in deep learning in particular, is to cherry-pick the most suitable for a given task, ensure they offer real improvements and deploy them appropriately.<\/p>\n<p>We don\u2019t see machine learning as a panacea for <em>all<\/em>\u200b data challenges. SciBiteAI offers an exciting addition to the SciBite suite and it is the combination of our tools for making data FAIR (Findable, Accessible, Interoperable, Reusable) across an organization and applying them to critical questions where we see SciBiteAI fulfilling a valuable need.<\/p>\n<section class=\"Section\"><\/section>\n<section class=\"Section Section--promoted\" data-total-count=\"11\" data-news-location-id=\"3329574\">\n<article class=\"Teaser\" readability=\"2.9473684210526\">\n<p class=\"Teaser-thumb\">\n<p> <a class=\"Teaser-ill\" data-widget=\"ImageCover\" href=\"https:\/\/www.outsourcing-pharma.com\/Library\/What-to-consider-when-working-with-CRFs\"><br \/>\n <img data-recalc-dims=\"1\" decoding=\"async\" data-src=\"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/09\/YkwDHl.png?w=640&ssl=1\" alt=\"What to consider when working with CRFs\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\"><\/a><\/p>\n<p> <span class=\"Teaser-icon Teaser-icon--red\"><svg class=\"Icon Icon--file-pdf\" aria-hidden=\"true\"><use xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"\/bundles\/wrbmgbfront\/sprite\/icons.svg#file-pdf\" \/><g \/><\/svg><\/span>\n <\/p>\n<div class=\"Teaser-text\" readability=\"31.157894736842\">\n<h3 class=\"Teaser-title\">\n <a href=\"https:\/\/www.outsourcing-pharma.com\/Library\/What-to-consider-when-working-with-CRFs\">What to consider when working with CRFs<\/a><br \/>\n <\/h3>\n<p class=\"Teaser-meta\">\n <span class=\"Teaser-info\">Formedix | 21-Sep-2020<br \/>\n | Technical \/ White Paper <\/span>\n <\/p>\n<p class=\"Teaser-intro\">\n For a study to be successful, data collected must be correct and complete. To be correct and complete, forms must be well planned with meticulous attention…\n <\/p>\n<\/p><\/div>\n<\/article>\n<article class=\"Teaser\" readability=\"2.01171875\">\n<p class=\"Teaser-thumb\">\n<p> <a class=\"Teaser-ill\" data-widget=\"ImageCover\" href=\"https:\/\/www.outsourcing-pharma.com\/Library\/Covance-Touchless-Drug-and-Device-Vigilance-Vision\"><br \/>\n <img data-recalc-dims=\"1\" decoding=\"async\" data-src=\"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/09\/N4vSxl.jpg?w=640&ssl=1\" alt=\"Covance Touchless Drug and Device Vigilance Vision\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\"><\/a><\/p>\n<p> <span class=\"Teaser-icon Teaser-icon--red\"><svg class=\"Icon Icon--file-pdf\" aria-hidden=\"true\"><use xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"\/bundles\/wrbmgbfront\/sprite\/icons.svg#file-pdf\" \/><g \/><\/svg><\/span>\n <\/p>\n<div class=\"Teaser-text\" readability=\"28.1640625\">\n<h3 class=\"Teaser-title\">\n <a href=\"https:\/\/www.outsourcing-pharma.com\/Library\/Covance-Touchless-Drug-and-Device-Vigilance-Vision\">Covance Touchless Drug and Device Vigilance Vision<\/a><br \/>\n <\/h3>\n<p class=\"Teaser-meta\">\n <span class=\"Teaser-info\">Covance Patient Safety | 01-Sep-2020<br \/>\n | Data Sheet <\/span>\n <\/p>\n<p class=\"Teaser-intro\">\n Automation tools enable organizations to identify patterns in structured and unstructured data and facilitate automation of the entire product vigilance…\n <\/p>\n<\/p><\/div>\n<\/article>\n<article class=\"Teaser\" readability=\"3.2248062015504\">\n<p class=\"Teaser-thumb\">\n<p> <a class=\"Teaser-ill\" data-widget=\"ImageCover\" href=\"https:\/\/www.outsourcing-pharma.com\/Library\/Lessons-Learned-Clinical-Trials-During-a-Pandemic\"><br \/>\n <img data-recalc-dims=\"1\" decoding=\"async\" data-src=\"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/09\/S7ILPm.jpg?w=640&ssl=1\" alt=\"Lessons Learned: Clinical Trials During a Pandemic\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\"><\/a><\/p>\n<p> <span class=\"Teaser-icon Teaser-icon--red\"><svg class=\"Icon Icon--file-pdf\" aria-hidden=\"true\"><use xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"\/bundles\/wrbmgbfront\/sprite\/icons.svg#file-pdf\" \/><g \/><\/svg><\/span>\n <\/p>\n<div class=\"Teaser-text\" readability=\"30.635658914729\">\n<h3 class=\"Teaser-title\">\n <a href=\"https:\/\/www.outsourcing-pharma.com\/Library\/Lessons-Learned-Clinical-Trials-During-a-Pandemic\">Lessons Learned: Clinical Trials During a Pandemic<\/a><br \/>\n <\/h3>\n<p class=\"Teaser-meta\">\n <span class=\"Teaser-info\">PCM TRIALS | 01-Sep-2020<br \/>\n | Technical \/ White Paper <\/span>\n <\/p>\n<p class=\"Teaser-intro\">\n In a recent survey of clinical trial professionals (including pharmaceutical sponsors, CROs, site representatives and vendors), respondents reported that…\n <\/p>\n<\/p><\/div>\n<\/article>\n<article class=\"Teaser\" readability=\"1.9818181818182\">\n<p class=\"Teaser-thumb\">\n<p> <a class=\"Teaser-ill\" data-widget=\"ImageCover\" href=\"https:\/\/www.outsourcing-pharma.com\/Library\/Global-Pediatric-Drug-Development-Collaboration-Part-Two\"><br \/>\n <img data-recalc-dims=\"1\" decoding=\"async\" data-src=\"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/09\/bEn4pp.jpg?w=640&ssl=1\" alt=\"Global Pediatric Drug Development Collaboration: Part Two\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\"><\/a><\/p>\n<p> <span class=\"Teaser-icon Teaser-icon--red\"><svg class=\"Icon Icon--file-pdf\" aria-hidden=\"true\"><use xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"\/bundles\/wrbmgbfront\/sprite\/icons.svg#file-pdf\" \/><g \/><\/svg><\/span>\n <\/p>\n<div class=\"Teaser-text\" readability=\"27.745454545455\">\n<h3 class=\"Teaser-title\">\n <a href=\"https:\/\/www.outsourcing-pharma.com\/Library\/Global-Pediatric-Drug-Development-Collaboration-Part-Two\">Global Pediatric Drug Development Collaboration: Part Two<\/a><br \/>\n <\/h3>\n<p class=\"Teaser-meta\">\n <span class=\"Teaser-info\">PRA Health Sciences | 09-Jul-2020<br \/>\n | Technical \/ White Paper <\/span>\n <\/p>\n<p class=\"Teaser-intro\">\n 70% of medicines given to children have never been studied in pediatric populations. Children who need treatments represent a relatively small population….\n <\/p>\n<\/p><\/div>\n<\/article>\n<\/section>\n<section class=\"Section\"><\/section>\n<div class=\"Layout-aside\">\n<section class=\"Section\"><\/section>\n<section class=\"Section adBox\"><\/section>\n<section class=\"Section adBox\"><\/section>\n<section class=\"Section adBox\"><\/section>\n<section class=\"Section Section--promoted\"><\/section>\n<section class=\"Section adBox\"><\/section>\n<section class=\"Section Section--promoted\" readability=\"-4.3076923076923\">\n<div class=\"Section-content Section-content--highlighted\" readability=\"5.7435897435897\">\n<p class=\"Section-subtitle\">Upcoming supplier webinars<\/p>\n<p class=\"Section-subtitle\">On-demand webinars<\/p>\n<\/p><\/div>\n<\/section>\n<\/div>\n<footer><\/footer>\n<p><noscript><\/p>\n<p><\/noscript><\/p>\n<p><noscript><\/p>\n<p><\/noscript><\/p>\n<p>Published at Thu, 24 Sep 2020 17:48:45 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers use artificial intelligence tools to predict loneliness Credit: CC0 Public Domain For the past…<\/p>\n","protected":false},"author":3,"featured_media":2863,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-2869","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/09\/sdy6gp.jpg?fit=800%2C480&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-Kh","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/2869","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=2869"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/2869\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/2863"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=2869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=2869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=2869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}