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<h2 class="hd hd-2 unit-title">This Course on Open Learning Library</h2>
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<h2 class="hd hd-2 unit-title">Before the Course Begins</h2>
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<h3 id="2018-hst-953-collaborative-data-science-in-medicine" style="text-rendering: optimizelegibility; margin: 30px 40px 15px 0px; padding: 0px; border: 0px; outline: 0px; font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; font-size: 1.2em; line-height: 1.2em; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; vertical-align: baseline; letter-spacing: 1px; text-transform: uppercase; -webkit-font-smoothing: antialiased;"><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;"><strong>Welcome to <em>HST.953X: COLLABORATIVE DATA SCIENCE IN MEDICINE</em>!</strong></span></h3>
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<h3><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;">Who this course is aimed at</span></h3>
<p><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;">In this course, we highlight the importance of a multidisciplinary approach to health data science. It is intended for front-line clinicians and public health practitioners, as well as computer scientists, engineers, and social scientists, whose goal is to understand health and disease better using digital data captured in the process of care.</span></p>
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<p><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;"> </span></p>
<h3><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;">Course Syllabus</span></h3>
<p><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;">Section 1 provides a general perspective about digital health data, their potential and challenges for research and use for retrospective analyses and modeling. Section 2 focuses on the Medical Information Mart for Intensive Care (MIMIC) database, curated by the Laboratory for Computational Physiology at MIT. The learners will have an opportunity to develop their analytical skills while following a research project, from the definition of a clinical question to the assessment of the analysis’ robustness. The last section is a collection of workshops around the applications of data science in healthcare.</span></p>
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<p><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;"> </span></p>
<h3><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;">Prerequisites</span></h3>
<p><span style="font-family: 'Open Sans', Verdana, Arial, Helvetica, sans-serif;"><span style="text-decoration: underline;"><strong>Experience with R is required (with Python and/or SQL desirable) unless the course is taken with computer scientists in the team.</strong><br /></span>We suggest this course be taken as part of a team consisting of clinicians and computer scientists or engineers. Learners from the healthcare sector are likely to have difficulties with the programming aspect while computer scientists and engineers will not be familiar with the clinical context of the exercises and workshops.</span></p>
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<p style="color: #313131; margin: 0px 0px 1.41575em; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 16px;">This video is presented by:</p>
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<h3>On behalf of MIT Critical Data, I welcome you to HST.953x. </h3>
<p>This course was created by members of MIT Critical Data, a global consortium that consists of healthcare practitioners, computer scientists, and engineers from academia, industry, and government. MIT Critical Data seeks to place data-driven research and learning at the front and center of the healthcare of individuals up to the level of global populations. We create cross-disciplinary communities to derive knowledge from the data routinely collected in the process of care. We seek to improve the understanding of both health and disease in all clinical and cultural contexts.</p>
<p>Research has been traditionally viewed as a purely academic undertaking, especially in limited-resource settings. Clinical trials, the gold standards of medical research, are expensive, difficult to perform well, and often result in findings limited by issues of subject inclusion and exclusion. They also take place primarily in countries which can afford them. Blood pressure thresholds for hypertension, or blood sugar targets for diabetics, are established based on research performed in a handful of countries. There is an implicit but inaccurate assumption that the validity of such studies carried out in the US and other Western countries fully and accurately generalize to patients around the world.</p>
<p>The digitalization of care presents an opportunity to engage healthcare practitioners from every country in pertinent and applicable research. This research is based on the partnership of clinicians and public health practitioners with computer scientists, engineers and social scientists, in an inspiring enterprise consisting of the creation and validation of medical knowledge.</p>
<p>Data is proliferating in diverse forms within the healthcare field, not only because of the adoption of digital health records, but also because of the growing use of wireless technologies for ambulatory monitoring. The world is abuzz with applications of data science in almost every field – commerce, transportation, banking, and more recently, healthcare. These breakthroughs are due to both rediscovered and newly created algorithms, powerful computers to run them, and most importantly, the availability of bigger and increasingly reliable and usable data to train the algorithms. This course provides an introductory survey of the data science tools employed in healthcare for these purposes. Instruction is carried out through a variety of proven hands-on workshops and exercises. </p>
<p>In our quest to fully leverage the data routinely collected in the process of care, we need to remind ourselves that the actual data elements are heavily influenced by long-standing social, cultural, and institutional biases. In an ideal world, only individual patient health and disease factors would determine — and guide the prediction of — clinical outcomes. However, we live in a world where the entered data elements, applied algorithms, and human interpretations and decisions can be knowingly or unconsciously biased. And unless the underlying inequities in our communities are addressed, such biases will perpetuate, and at times magnify, existing health disparities.</p>
<p>This course is a labor of love among friends from around the world. The most daunting global health issues right now are the result of interconnected crises: global warming, the Covid pandemic, the economic fallouts of the pandemic, political extremism and polarization, and systemic racism. However, we also live in a time of powerful advances in information technology, artificial intelligence, and molecular biology that can be applied to these formidable health problems.</p>
<p>In this course, we highlight the importance of a multidisciplinary approach to knowledge generation. Every patient encounter, digitally documented, stored, and analyzable, is an opportunity that we can learn from. In this context, every healthcare practitioner, in partnership with data scientists, should and will become a contributor and a custodian, and not merely a passive recipient, of the medical knowledge system. </p>
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<h2 class="hd hd-2 unit-title">Textbook 1 (External Resource)</h2>
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<h3>Leveraging Data Science for Global Health</h3>
<p>A digital copy of the accompanying textbook to this course, also written by MIT Critical Data and published by Springer, can be downloaded for free <a href="https://link.springer.com/book/10.1007/978-3-030-47994-7" target="[object Object]">here</a>.</p>
<p><img height="676" width="450" src="/assets/courseware/v1/26cfaed32d2f7ac542c27994db12f237/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-09-02_at_22.12.29.png" alt="" /> </p>
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<h3>Secondary Analysis of Electronic Health Records</h3>
<p>Feel free to also check out our previous textbook about the secondary analysis of electronic health records, featuring a "cookbook" for data science. The digital copy of the book is available for free <a href="https://www.springer.com/gp/book/9783319437408" target="[object Object]">here</a>.</p>
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<p>If you are interested in designing or implementing a health information and communication technology (ICT) solution in the developing world, please check out our previous online course through the link below:</p>
<p><a href="https://www.edx.org/course/global-health-informatics-to-improve-quality-of-ca" target="_blank">HST.936x - Global Health Informatics to Improve Quality of Care.</a></p>
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<p><img height="765" width="999" src="/assets/courseware/v1/a4fdc6ceece51c8a32f334c921531a40/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-08-23_at_16.11.39.png" alt="" /></p>
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<h2 class="hd hd-2 unit-title">Content and Course Creators</h2>
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<p>We, the course instructors, would like to thank everyone who helped build this course by either creating the content or converting an on-site course at MIT into an edX format.</p>
<p><strong><span style="text-decoration: underline;">Content Creators</span></strong><br />Aaron Kauffman<br />Aldo Alvarez<br />Alistair Johnson<br />Allan Walkey<br />Alon Dagan<br />Amber Nigam<br />Andre Putra<br />Andrew Beam<br />Ari Moskowitz<br />Brian Carney<br />Chaitanya Shivade<br />Chen Xi<br />Christopher Cosgriff<br />Christina Chen<br />Corwin Zigler<br />Daniel Gruhl<br />David Bates<br />Donny Cheung<br />Ed Kao<br />Euma Ishii<br />Felipe Torres<br />Finale Doshi-Velez<br />Guillaume Krugener<br />Jen Stevens<br />Jesse Rafa<br />Johanna Katz<br />John Danziger<br />John Halamka<br />Joshua Hartshorne<br />Joy Wu<br />Judith Lok<br />Kenneth Paik<br />Kimiko Huang<br />Kiri Wagstaff<br />Kristen Severson<br />Kun-Hsing Yu<br />Leo Anthony Celi<br />Lucas Bulgarelli<br />Marianne Slight<br />Marie-Laure Charpignon<br />Marta Fernandesa<br />Marzyeh Ghassemi<br />Mattheiu Komarowski<br />Miguel Armengol<br />Miguel Hernan<br />Mike Hughes<br />Ming Jack Po<br />Ming Yu Lu<br />Minnan Xu<br />Mohammad Ghassemi<br />Ned McCague<br />Nikete Della<br />Nikhil Shankar<br />Omar Badawi<br />Patrick Kimes<br />Pragati Jaiswal<br />Rob Stretch<br />Rodrigo Deliberato<br />Roger Mark<br />Ryan Kindle<br />Ryo Uchimodo<br />Satra Gosh<br />Shawn Sterling<br />Sicheng Hao<br />Sunil Nair<br />Tom Pollard<br />Tristan Naumann<br />Wei-Hung Weng<br />Willie Boag<br />Yue-Hsin</p>
<p><span style="text-decoration: underline;"><strong>Course Creators</strong><br /></span>Aldo Arevalo<br />Alon Dagan<br />Amber Nigam<br />Amelie Mathusek<br />Andre Silva<br />Daniel Ebner<br />Daniel Glauert<br />Eric Yamga<br />Grigorich Schleifer<br />Haroun Chahed<br />Jonathan Riesner<br />Julia Situ<br />Kimiko Huang<br />Lawrence Baker<br />Liam McCoy<br />Louis Agha-Mir-Salim<br />Marie Charpignon<br />Marta Fernandes<br />Mathew Samuel<br />Philipp Klocke<br />Pragati Jaiswal<br />Ryan Kindle<br />Shrey Lakhotia<br />Sreekar Mantena<br />Wesley Yeung<br />Yueh-Hsun Chuang<br />Ziyi Hou</p>
<p><span style="text-decoration: underline;"><strong>Course Instructors</strong><br /></span>Julia Situ<br />Kenneth Paik<span style="text-decoration: underline;"><br /></span>Louis Agha-Mir-Salim<br />Leo Anthony Celi<br />Marie-Laure Charpignon<br />Wesley Yeung</p>
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