Fei Wang is currently a tenured Professor of Health Informatics in Department of Population Health Sciences at Weill Cornell Medicine (WCM), where he also holds a secondary appointment as a Professor in Department of Emergency Medicine. Dr. Wang is the Founding Director of the WCM Institute of AI for Digital Health (AIDH) and an Adjunct Scientist at Hospital for Special Surgery (HSS). His research interest is machine learning and artificial intelligence in biomedicine. Dr. Wang has published over 350 papers on the major venues of AI and biomedicine, which have received more than 30K citations to date. His H-index is 83. Dr. Wang is an elected fellow of American Medical Informatics Association (AMIA), American College of Medical Informatics (ACMI) and International Academy of Health Sciences and Informatics (IAHSI), and a distinguished member of Association for Computing Machinery (ACM).
Course Overview
In the healthcare sector, patient data is abundant. Machine learning can transform this data into a powerful tool for prediction and analysis. In this course, you will explore supervised and unsupervised learning, two key machine learning approaches that can help you maximize your data's potential. Before addressing healthcare challenges with machine learning, it's essential to begin with high-quality data. You'll examine and practice the key steps to clean and prepare raw data, ensuring it's ready for effective machine analysis.
Once you've mastered these data preparation processes, you'll be ready to apply machine learning to healthcare analysis. You'll use supervised learning techniques to predict whether a patient is likely to experience sepsis. You'll also leverage unsupervised learning methods to identify similar subtypes within a large group of patients. By the end of the course, you'll realize how machine learning can improve efficiency for medical professionals and personalize patient care.
Students must have intermediate proficiency in Python programming and machine learning to succeed in this course.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Designing Digital Healthcare Tools
- Data Management in Healthcare
Key Course Takeaways
- Examine supervised and unsupervised learning algorithms and discuss how to apply them in healthcare
- Apply supervised learning algorithms in clinical decision support systems
- Apply unsupervised learning algorithms in stratified patient management
How It Works
Course Author
Who Should Enroll
- Data scientists
- Medical and health services managers
- Database and IT data architects
- Data engineers
- Digital transformation managers
- Clinicians with experience in informatics
- Biomedical and clinical informatics fellows
- Aspiring medical database managers or administrators
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