Fall 2025 Course Topic: Machine Learning for Healthcare
This course aims to introduce advanced machine learning techniques for solving different tasks in the healthcare domain, including risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.
Topics include:
- missing EHR (electronic health records)
- data completion
- longitudinal data analysis
- time-series analysis
- unstructured clinical notes mining
- medical natural language processing
- medical image analysis
- multi-sourced data integration
- deep learning
- transfer learning
- Semester: Fall 2025
- Instructor: Fenglong Ma
- Who: Students who can use this course as:
- an advanced elective/additional course for all DS students (ENGR, IST, SCIEN)
- an application focus course for HCDD
- a supporting course for SRA
- When: MW 2:30 p.m. - 3:45 p.m.
- Where: E165 Westgate Building
- Credits: 3.0
- Prerequisites: DS 220
- LionPATH Class Number: 27766
- Recognize the unique properties and challenges of various medical data types, including structured EHRs, time-series data, clinical text, and medical images.
- Utilize machine learning methods for EHR modeling, time-series analysis, medical NLP, and medical image processing, effectively integrating structured and unstructured data sources.
- Implement fundamental machine learning models alongside advanced techniques such as deep learning, transfer learning, and representation learning to improve healthcare predictions and model adaptability.
- Apply machine learning algorithms to real-world healthcare problems using Python and PyTorch, developing practical skills through coding and experimentation.