Computer Science Department, University of Illinois at Urbana-Champaign
There are about 10,000 known human diseases, yet human doctors are only able to recall a fraction of them at any given moment. Operational waste and inefficiencies in the healthcare system are vastly overlooked. But maybe, with the help of data analytics, we can overcome all these issues. Today, in healthcare, large amounts of multi-modal health data is becoming more accessible. Electronic health records, genetic, imaging, and smartwatch data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment.
This graduate level course will consist of paper readings, presentations, and student projects. Students write critiques, make presentations, and create an academic paper suitable for a workshop or conference. We will review the recent advances in the area of health data analysis. Reading selections broadly cover the clinical, genetic, image, and wearables data analysis. Students are expected to have a working knowledge of machine learning, data mining, and programming skills to carry an implementation of a final project (preferably in Python, but all languages are welcome). The project is extremely hands-on. You will experience firsthand all of the journeys a data scientist goes through: data ambiguity, missing data, anomalies, skewness, predictive models, descriptive models, etc.
Prerequisites: working knowledge of machine learning, data mining, and programming skills
Syllabus (start here for an overview, instructions, and policies)