Collaborative Data Science for Healthcare
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 should attend?
Experience with R, Python and/or SQL is required unless the course is taken with computer scientists in the team.
- 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.
Course delivery details
This course is offered through Massachusetts Institute of Technology, a partner institute of EdX.
2-3 hours per week
- Verified Track -$49
- Audit Track - Free
Certification / Credits
What you'll learn
- Principles of data science as applied to health
- Analysis of electronic health records
- Artificial intelligence and machine learning in healthcare
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