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Louis Agha-Mir-Salim, Leo Anthony Celi, Marie-Laure Charpignon, Kenneth Eugene Paik, Julia Situ, and Wesley Yeung

Research has been traditionally viewed as a purely academic undertaking, especially in limited-resource healthcare systems. Clinical trials, the hallmark of medical research, are expensive to perform, and take place primarily in countries which can afford them. Around the world, the blood pressure thresholds for hypertension, or the blood sugar targets for patients with diabetes, are established based on research performed in a handful of countries. There is an implicit assumption that the findings and validity of studies carried out in the US and other Western countries generalize to patients around the world.

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Research has been traditionally viewed as a purely academic undertaking, especially in limited-resource healthcare systems. Clinical trials, the hallmark of medical research, are expensive to perform, and take place primarily in countries which can afford them. Around the world, the blood pressure thresholds for hypertension, or the blood sugar targets for patients with diabetes, are established based on research performed in a handful of countries. There is an implicit assumption that the findings and validity of studies carried out in the US and other Western countries generalize to patients around the world.

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, that seeks to place data and research at the front and center of healthcare operations.

Big data is proliferating in diverse forms within the healthcare field, not only because of the adoption of electronic 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 rediscovered algorithms, powerful computers to run them, and most importantly, the availability of bigger and better data to train the algorithms. This course provides an introductory survey of data science tools in healthcare through several hands-on workshops and exercises.

Who this course is aimed at

The most daunting global health issues right now are the result of interconnected crises. 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.

We highly recommend that 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 the computer scientists and engineers will not be familiar with the clinical context of the exercises and workshops.

The MIT Critical Data team would like to acknowledge the contribution of the following members: Aldo Arevalo, Alistair Johnson, Alon Dagan, Amber Nigam, Amelie Mathusek, Andre Silva, Chaitanya Shivade, Christopher Cosgriff, Christina Chen, Daniel Ebner, Daniel Gruhl, Eric Yamga, Grigorich Schleifer, Haroun Chahed, Jesse Raffa, Jonathan Riesner, Joy Tzung-yu Wu, Kimiko Huang, Lawerence Baker, Marta Fernandes, Mathew Samuel, Philipp Klocke, Pragati Jaiswal, Ryan Kindle, Shrey Lakhotia, Tom Pollard, Yueh-Hsun Chuang, Ziyi Hou.

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What's inside

Learning objectives

  • Principles of data science as applied to health
  • Analysis of electronic health records
  • Artificial intelligence and machine learning in healthcare

Syllabus

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 the workshops around the applications of data science in healthcare.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by recognized experts in data science and healthcare
Explores core concepts of data science as applied to healthcare, which is vital for a range of roles in the field
Offers practical, hands-on experience through workshops and exercises
Suitable for both clinicians and computer scientists/engineers, reflecting the interdisciplinary nature of healthcare data science
Emphasizes the importance of data science in addressing global health challenges

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Collaborative Data Science for Healthcare with these activities:
Review Principles of Data Science
Sharpen your understanding of the foundational concepts of data science, preparing you for the course's advanced topics.
Browse courses on Data Science
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  • Recall key concepts from previous data science courses or materials
  • Review online resources and tutorials on data science principles
Review Data Science
Review key data science concepts and terminology used in healthcare research.
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  • Revisit linear regression, logistic regression, and decision trees.
  • Review supervised and unsupervised learning methods.
  • Explore the applications of data science in healthcare.
Participate in Data Science Study Groups
Engage with peers to discuss concepts, share insights, and enhance your understanding of the course material.
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  • Join or create a study group with fellow students
  • Meet regularly to discuss course topics and work through exercises together
Five other activities
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Become a Peer Mentor
Enhance your understanding of the course material while helping others by volunteering as a peer mentor.
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Show steps
  • Apply to become a peer mentor through the university or course platform
  • Provide support and guidance to fellow students
Explore Data Visualization Libraries
Gain hands-on experience with popular data visualization libraries, enhancing your ability to present insights effectively.
Browse courses on Data Visualization
Show steps
  • Identify suitable data visualization libraries for your projects
  • Follow online tutorials to learn the core functionalities of these libraries
  • Practice creating visualizations using sample datasets
Attend Data Science Workshops
Expand your knowledge and skills by participating in workshops focused on specific aspects of data science relevant to the course.
Browse courses on Data Science
Show steps
  • Identify workshops related to course topics
  • Register and attend these workshops
Develop Data Analysis Case Studies
Demonstrate your proficiency in data analysis by creating case studies that illustrate your understanding of real-world scenarios.
Browse courses on Data Analysis
Show steps
  • Select a healthcare-related dataset for analysis
  • Apply data analysis techniques to identify trends and patterns
  • Summarize your findings in a written report or presentation
Contribute to Open Source Data Science Projects
Gain practical experience and contribute to the data science community by participating in open source projects.
Browse courses on Open Source
Show steps
  • Identify open source data science projects aligned with your interests
  • Review the project documentation and contribute code or other resources

Career center

Learners who complete Collaborative Data Science for Healthcare will develop knowledge and skills that may be useful to these careers:
Data Scientist
Develop and apply data science techniques to solve problems in healthcare, such as disease diagnosis, drug discovery, and patient care management. This course provides a comprehensive overview of data science tools and techniques, as well as hands-on experience with real-world healthcare data.
Machine Learning Engineer
Design and implement machine learning algorithms to improve healthcare outcomes, such as predicting patient risk, identifying patterns in medical data, and developing personalized treatment plans. This course provides a solid foundation in machine learning techniques, as well as experience with healthcare-specific data.
Clinical Data Analyst
Gather and analyze data from patient health records with the aim of improving health outcomes. This course provides a strong foundation in health data science, including data analysis and mining techniques, as well as an understanding of the clinical context of healthcare data.
Healthcare Consultant
Advise healthcare organizations on how to use data and analytics to improve patient care, reduce costs, and enhance operational efficiency. This course provides a deep understanding of the healthcare industry, as well as the tools and techniques needed to analyze and interpret healthcare data.
Epidemiologist
Investigate the causes and distribution of diseases and injuries in populations, and develop strategies to prevent and control them. This course provides a foundation in epidemiology, as well as experience with data analysis and interpretation.
Medical Writer
Develop and write scientific and technical documents, such as research papers, clinical guidelines, and patient education materials. This course provides a strong foundation in scientific writing, as well as an understanding of the healthcare industry and medical terminology.
Public Health Analyst
Analyze and interpret data to identify and address public health issues, such as disease outbreaks, environmental hazards, and access to healthcare. This course provides a strong foundation in data analysis and visualization techniques, as well as an understanding of the social and behavioral factors that influence health.
Clinical Research Coordinator
Manage and coordinate clinical research studies, including data collection, analysis, and reporting. This course provides a strong understanding of the clinical research process, as well as experience with data management and analysis.
Health Policy Analyst
Analyze and evaluate healthcare policies and programs to improve health outcomes and reduce costs. This course provides a strong understanding of health policy, as well as experience with data analysis and interpretation.
Nonprofit Program Manager
Manage and implement programs and services for nonprofit organizations, such as healthcare clinics, community health centers, and patient advocacy groups. This course provides a strong understanding of nonprofit management, as well as experience with data analysis and interpretation.
Global Health Consultant
Provide technical assistance and support to healthcare organizations in developing countries to improve health outcomes and strengthen health systems. This course provides a deep understanding of global health issues, as well as experience with data analysis and interpretation.
Health Educator
Develop and deliver educational programs and materials to promote health and prevent disease. This course provides a strong understanding of health education, as well as experience with data analysis and interpretation.
Health Informatics Specialist
Design and implement health information systems, such as electronic health records and patient portals. This course provides a strong understanding of health informatics, as well as experience with data management and analysis.
Medical Librarian
Provide access to and manage medical information resources, such as books, journals, and databases. This course provides a strong understanding of medical librarianship, as well as experience with data management and analysis.
Healthcare Administrator
Manage the operations of healthcare organizations, such as hospitals, clinics, and nursing homes. This course provides a strong understanding of healthcare administration, as well as experience with data analysis and interpretation.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Collaborative Data Science for Healthcare.
Provides a comprehensive overview of the field of artificial intelligence in healthcare, covering topics such as natural language processing, computer vision, and deep learning. It valuable resource for students and researchers in the field.
Provides a comprehensive overview of the field of data science for healthcare, covering topics such as data management, statistical analysis, and machine learning. It valuable resource for students and researchers in the field.
Explores the applications of artificial intelligence in healthcare, covering topics such as natural language processing, computer vision, and decision support systems.
This practical guide provides a step-by-step approach to designing and implementing data science projects in any industry, including healthcare.
This textbook provides a gentle introduction to statistical learning, focusing on practical applications and using R for data analysis.

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