Data Science in Health: Exploring Digital Health Data

Course Description

This advanced-level course, offered by MITx, is an exciting opportunity to delve into the rapidly evolving field of health data science. Created by members of MIT Critical Data, a global consortium of healthcare practitioners, computer scientists, and engineers, this course provides a comprehensive introduction to data science tools in healthcare through hands-on workshops and exercises.

The course challenges the traditional view of research as a purely academic pursuit, especially in limited-resource healthcare systems. It explores how big data is revolutionizing healthcare, from electronic health records to wireless technologies for ambulatory monitoring. By focusing on the application of data science in healthcare, this course equips learners with the skills to tackle interconnected global health crises using a multidisciplinary approach.

What students will learn

• Principles of data science as applied to health
• Analysis of electronic health records
• Artificial intelligence and machine learning in healthcare
• How to approach health data science from a multidisciplinary perspective
• Techniques for analyzing and interpreting digital health data
• Understanding the potential and challenges of using digital health data for research and retrospective analyses

Pre-requisites

Experience with R, Python, and/or SQL is required unless the course is taken with computer scientists in the team. The course is designed for a multidisciplinary approach, so it's highly recommended that learners form teams consisting of clinicians and computer scientists or engineers.

Course Coverage

• Introduction to digital health data and its potential in healthcare
• Challenges and opportunities in health data science
• Analysis of electronic health records using various tools and techniques
• Application of artificial intelligence and machine learning in healthcare settings
• Hands-on workshops and exercises using real-world health data
• Exploration of the Medical Information Mart for Intensive Care (MIMIC) database
• Development of analytical skills through a research project
• Assessment of analysis robustness in health data science

Target Audience

This course is aimed at a diverse audience, including:

• Front-line clinicians and public health practitioners
• Computer scientists and engineers
• Social scientists
• Anyone interested in understanding health and disease better using digital data captured in the process of care

The course is particularly beneficial for multidisciplinary teams, as it combines clinical knowledge with technical skills.

Real-world Applications

• Improving patient care through data-driven decision making
• Developing more accurate predictive models for disease outcomes
• Enhancing public health interventions using population-level data
• Conducting more efficient and cost-effective clinical trials
• Creating personalized treatment plans based on individual patient data
• Identifying trends and patterns in health data to inform policy decisions
• Developing innovative health technologies and applications

Syllabus

The course is divided into three main sections:

Section 1: General perspective on digital health data

• Potential and challenges of digital health data
• Use of data for retrospective analyses and modeling

Section 2: Medical Information Mart for Intensive Care (MIMIC) database

• Introduction to the MIMIC database
• Developing analytical skills through a research project
• Defining a clinical question
• Conducting analysis
• Assessing the robustness of the analysis

Section 3: Workshops on applications of data science in healthcare

• Various hands-on exercises and case studies
• Practical application of learned concepts