We are pleased to announce the launching of a new course, Applied Data Science and Machine Learning, for Health & Clinical Informatics (HCIN) majors in our Biomedical Informatics Graduate Program. While the course will be given a general number (BMI 507/607) initially this year, it will be given a permanent number starting next year.
The goal of this new course is to provide a conceptual understanding of the application of data science and machine learning in health and clinical medicine. While the course will have some programming activity (requiring Python programming as a prerequisite), it will focus on a hands-on, high-level view of the different types of machine learning methods and their applications. It will also cover the topics of data management and exploration, pitfalls in building and deploying models, and critical appraisal of clinical machine learning literature. The course will aim to provide an in-depth understanding of the broad issues related to machine learning for both those who will develop machine learning models as well as those who will work alongside those who do. It is our intent that this course will help students understand the process of creating, implementing, and evaluating machine learning applications in health and clinical settings.
The textbook for the course will be: Hoyt, R. and Muenchen, R. (Eds.), 2019. Introduction to Biomedical Data Science, Lulu.com. The course syllabus provides further details on the topics to be covered.
The content of the course will be derived from faculty discussion as well as a survey that was distributed to students. Among the topics to be included are:
- Data sources – electronic health records, registries (e.g., N3C, AllOfUs), patient-generated, social media, public health
- Data preparation (wrangling) – cleaning, quality analysis, feature selection, de-biasing
- Exploratory data analysis – summaries, correlations, visualizations
- Machine learning approaches and models – supervised, unsupervised, reinforcement, deep learning
- Software and tools available
- Common pitfalls and misunderstandings of applying machine learning
- Critical appraisal of clinical machine learning literature
- Ethical issues and challenges
The course will culminate in a project developing and evaluating a machine learning model based on a clinical data set.
The 3-credit course will be taught in the OHSU spring academic quarter, which runs from late March to early June. The lead instructors will be Steven Chamberlin, ND and William Hersh, MD, with other department faculty contributing. As with all courses in the HCIN major, it will be mostly online and asynchronous, with some option synchronous activities (which will be recorded for those not able to attend). This course will be complementary to other data science-related courses in the HCIN major, including:
- BSTA 525/625 – Introduction to Biostatistics
- BMI 540/640 – Computer Science and Programming for Clinical Informatics
- BMI 544/644 – Databases
- BMI 524/624 – Data Analytics for Healthcare
- BMI 516/616 – Standards/Interoperability in Healthcare
- BMI 537/637 – Healthcare Quality
- BMI 525/625 – Principles and Practice of Data Visualization
The course is not meant to be a substitute for the sequence of courses available in the other major in our program, Bioinformatics & Computational Biomedicine (BCB), whose offerings include:
- BMI 551/651 – Statistical Methods
- BMI 531/631 – Probability and Statistical Inference
- BMI 543/643 – Machine Learning
- BMI 525/625 – Principles and Practice of Data Visualization
- (Course Number TBD) Data Science Programming
(All of these courses are described further in our online course catalog.)
The BCB courses are focused on more technical aspects of data science, as well as applications within genomics and computational biology. We anticipate that there will be students in both majors who will take this new course, and will be inspired to take further technical courses in the BCB sequence.
