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<!DOCTYPE html>
<html lang="en"><head>
<script src="aiims_covid_files/libs/clipboard/clipboard.min.js"></script>
<script src="aiims_covid_files/libs/quarto-html/tabby.min.js"></script>
<script src="aiims_covid_files/libs/quarto-html/popper.min.js"></script>
<script src="aiims_covid_files/libs/quarto-html/tippy.umd.min.js"></script>
<link href="aiims_covid_files/libs/quarto-html/tippy.css" rel="stylesheet">
<link href="aiims_covid_files/libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
<link href="aiims_covid_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
<meta name="generator" content="quarto-1.0.36">
<meta name="author" content="Manika Lamba">
<meta name="dcterms.date" content="2022-10-13">
<title>Visualizing the Pace of COVID-19 Research</title>
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
<link rel="stylesheet" href="aiims_covid_files/libs/revealjs/dist/reset.css">
<link rel="stylesheet" href="aiims_covid_files/libs/revealjs/dist/reveal.css">
<style>
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<link rel="stylesheet" href="aiims_covid_files/libs/revealjs/dist/theme/quarto.css" id="theme">
<link rel="stylesheet" href="assets/syntax-highlight.css">
<link rel="stylesheet" href="assets/custom.css">
<link rel="stylesheet" href="assets/pacman.css">
<link href="aiims_covid_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.css" rel="stylesheet">
<link href="aiims_covid_files/libs/revealjs/plugin/reveal-menu/menu.css" rel="stylesheet">
<link href="aiims_covid_files/libs/revealjs/plugin/reveal-menu/quarto-menu.css" rel="stylesheet">
<link href="aiims_covid_files/libs/revealjs/plugin/quarto-support/footer.css" rel="stylesheet">
<style type="text/css">
.callout {
margin-top: 1em;
margin-bottom: 1em;
border-radius: .25rem;
}
.callout.callout-style-simple {
padding: 0em 0.5em;
border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
display: flex;
}
.callout.callout-style-default {
border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
}
.callout .callout-body-container {
flex-grow: 1;
}
.callout.callout-style-simple .callout-body {
font-size: 1rem;
font-weight: 400;
}
.callout.callout-style-default .callout-body {
font-size: 0.9rem;
font-weight: 400;
}
.callout.callout-captioned.callout-style-simple .callout-body {
margin-top: 0.2em;
}
.callout:not(.callout-captioned) .callout-body {
display: flex;
}
.callout:not(.no-icon).callout-captioned.callout-style-simple .callout-content {
padding-left: 1.6em;
}
.callout.callout-captioned .callout-header {
padding-top: 0.2em;
margin-bottom: -0.2em;
}
.callout.callout-captioned .callout-caption p {
margin-top: 0.5em;
margin-bottom: 0.5em;
}
.callout.callout-captioned.callout-style-simple .callout-content p {
margin-top: 0;
}
.callout.callout-captioned.callout-style-default .callout-content p {
margin-top: 0.7em;
}
.callout.callout-style-simple div.callout-caption {
border-bottom: none;
font-size: .9rem;
font-weight: 600;
opacity: 75%;
}
.callout.callout-style-default div.callout-caption {
border-bottom: none;
font-weight: 600;
opacity: 85%;
font-size: 0.9rem;
padding-left: 0.5em;
padding-right: 0.5em;
}
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padding-left: 0.5em;
padding-right: 0.5em;
}
.callout.callout-style-simple .callout-icon::before {
height: 1rem;
width: 1rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 1rem 1rem;
}
.callout.callout-style-default .callout-icon::before {
height: 0.9rem;
width: 0.9rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 0.9rem 0.9rem;
}
.callout-caption {
display: flex
}
.callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
.callout.no-icon::before {
display: none !important;
}
.callout.callout-captioned .callout-body > .callout-content > :last-child {
margin-bottom: 0.5rem;
}
.callout.callout-captioned .callout-icon::before {
margin-top: .5rem;
padding-right: .5rem;
}
.callout:not(.callout-captioned) .callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
/* Callout Types */
div.callout-note {
border-left-color: #4582ec !important;
}
div.callout-note .callout-icon::before {
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}
div.callout-note.callout-style-default .callout-caption {
background-color: #dae6fb
}
div.callout-important {
border-left-color: #d9534f !important;
}
div.callout-important .callout-icon::before {
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}
div.callout-important.callout-style-default .callout-caption {
background-color: #f7dddc
}
div.callout-warning {
border-left-color: #f0ad4e !important;
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<div class="reveal">
<div class="slides">
<section id="title-slide" class="center">
<h1 class="title">Visualizing the Pace of COVID-19 Research</h1>
<p class="subtitle">Experimental Study of AIIMS, New Delhi</p>
<p class="author">Manika Lamba</p>
<p class="institute">SIS Annual Convention</p>
<p class="date">2022-10-13</p>
</section>
<section id="introduction" class="title-slide slide level1 center">
<h1>Introduction</h1>
<ul>
<li><p>Scientists have been studying the human coronavirus since its discovery in the 1960s</p></li>
<li><p>With the emergence of a new variant of coronavirus in 2019, it became important to devote a significant amount of time to reading and identifying relevant studies to better understand COVID-19</p></li>
<li><p>It is urgent to sort out important, effective, and meaningful information from large databases to guide scientific research and promote the proper control, prevention, diagnosis, and treatment of COVID-19</p></li>
</ul>
</section>
<section id="topic-modeling" class="title-slide slide level1 center">
<h1>Topic Modeling</h1>
<ul>
<li><p>Topic modeling acts as a text mining approach to understand, organize, process, extract, manage, and summarize knowledge. It is an excellent tool for discovery and to uncover evidence already present in the text</p></li>
<li><p>A topic can be defined as the main idea discussed in a text, i.e., the theme or subject of different granularities</p></li>
<li><p>There are no machine-readable annotations that can tell the topic modeling programs about the semantic meaning of the words in the text</p></li>
<li><p>It infers abstract topics based on similar patterns of word usage in each documents</p></li>
</ul>
<div class="footer">
</div>
</section>
<section id="structural-topic-modeling" class="title-slide slide level1 center">
<h1>Structural Topic Modeling</h1>
<div class="columns">
<div class="column" style="width:60%;">
<ul>
<li>STM allow us to incorporate metadata into our model and uncover how different documents might talk about the same underlying topic using different word choices</li>
<li>In LDA, our topic prevalence and content came from Dirichlet distributions with hyperparameters we set in advance — sometimes referred to as <em>a</em> and <em>b</em></li>
</ul>
</div><div class="column" style="width:40%;">
<p><img data-src="images/stm%20model.png" width="400"></p>
</div><div class="footer">
<p>Plate Diagram for Structural Topic Model (Roberts et al., 2013)</p>
</div>
</div>
</section>
<section id="stm-continue" class="title-slide slide level1 center">
<h1>STM (Continue)</h1>
<ul>
<li><p>With STM, our topic prevalence and content come from document metadata</p></li>
<li><p>The matrix of document metadata used to generate <span style="color: blue;">topic prevalence is “X”</span> and the matrix of document metadata used to generate <span style="color: blue;">topic contents is “Y”</span>, where X=Y (i.e. we’re using the same metadata in both cases), <em>d</em> is the number of documents in the corpus and <em>p</em> is the number of metadata features</p></li>
</ul>
</section>
<section id="need-of-the-study" class="title-slide slide level1 center">
<h1>Need of the Study</h1>
<p>This study will help to <span style="color: blue;">(i) determine the core topics published by which AIIMS, New Delhi researchers related to COVID-19</span>, and <span style="color: red;">(ii) visualize the results using a dashboard and storyboard</span></p>
</section>
<section id="methodology" class="title-slide slide level1 center">
<h1>Methodology</h1>
<ul>
<li><p>Data was retrieved from Web of Science, Scopus, and PubMed databases for the query, “corona” OR “covid-19” OR “coronavirus” restricted to AIIMS, New Delhi</p></li>
<li><p>Search was performed in April 2021</p></li>
<li><p>Metadata for 853 studies were identified from all three databases which were then merged and removed for duplicates using the bibliometrix package in R</p></li>
<li><p>After the preliminary cleaning, a total of 388 publications were finalized for the period 2010-2021 from 199 journals for the study. The structural topic model was performed using stm package in R</p></li>
</ul>
<div class="footer">
</div>
</section>
<section>
<section id="findings" class="title-slide slide level1 center" data-background-color="#006dae">
<h1>Findings</h1>
<div class="footer">
</div>
</section>
<section id="core-topics-in-covid-19-research" class="slide level2">
<h2>Core Topics in COVID-19 Research</h2>
<img data-src="images/stm2.png" width="5000" class="r-stretch quarto-figure-center"><div class="footer">
</div>
</section></section>
<section id="topic-correlation" class="title-slide slide level1 center">
<h1>Topic Correlation</h1>
<div class="columns">
<div class="column" style="width:50%;">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><img data-src="images/corr.png" width="600"></p>
</figure>
</div>
</div><div class="column" style="width:50%;">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><img data-src="images/topics.png" width="600"></p>
</figure>
</div>
</div>
</div>
<div class="footer">
</div>
</section>
<section>
<section id="visualization-of-topics" class="title-slide slide level1 center">
<h1>Visualization of Topics</h1>
<ul>
<li><p>Output of topic modeling is not entirely human-readable, and one way to understand the results is through visualization</p></li>
<li><p>Therefore, we constructed a storyboard and dashboard that is human-readable and easy to comprehend as well as interpret</p></li>
</ul>
</section>
<section id="dashboard" class="slide level2">
<h2>Dashboard</h2>
<div class="quarto-figure quarto-figure-center">
<figure>
<p><a href="https://manika-lamba.github.io/stm/"><img data-src="images/dashboard.png" width="1700"></a></p>
</figure>
</div>
<div class="footer">
<p>https://manika-lamba.github.io/stm/</p>
</div>
<aside class="notes">
<p>When you click on the figure, a new window will open in your browser where you can interact and visualizes the changes by altering various parameters. Recommended settings are: X-axis = TI; Y-axis = topic of choice; Radius = topic chosen; Color = PY. The titles with topic proportion more than 0.50 (Y-axis) should be considered as the representative titles for that particular topic.</p>
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<section id="storyboard" class="slide level2">
<h2>Storyboard</h2>
<img data-src="images/stroyboard.png" width="1700" class="r-stretch quarto-figure-center"><div class="footer">
<p>https://manika-lamba.shinyapps.io/covid_aiims/</p>
</div>
</section>
<section id="conclusion" class="slide level2">
<h2>Conclusion</h2>
<ul>
<li><p>Topic modeling and data visualization are critical for exploring and communicating information effectively and helping researchers to continue to progress</p></li>
<li><p>The study presents an overview of early studies of the COVID-19 crisis published from AIIMS, New Delhi at different scales including topic variation, and their inner interactions</p></li>
<li><p>It also identifies papers that are regarded as the cornerstones for different topics in the development of COVID-19 research published by AIIMS, New Delhi</p></li>
</ul>
</section>
<section id="conclusion-continue" class="slide level2">
<h2>Conclusion (Continue)</h2>
<ul>
<li><p>The results unveil the focus of scientific research, thereby giving deep insights into how the top Indian medical society contributes to combating the COVID-19 pandemic</p></li>
<li><p>The methodology from this study can be applied to trace the literature of any field on any topic of interest</p></li>
<li><p>This study can be used to analyze large datasets of COVID-19 literature to investigate and visualize the ongoing advancements of early scientific research on COVID-19 from the perspective of Artificial Intelligence</p></li>
</ul>
</section>
<section id="thank-you" class="slide level2">
<h2>Thank you!</h2>
<p><br></p>
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