Applications of Machine Learning in Medicine
Program

With an emphasis on real-world utility and responsible AI, gain the skills to design, evaluate, and deploy machine learning solutions in healthcare. Online, on your schedule.

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Transform your impact in healthcare with machine learning.

Applications of Machine Learning in Medicine is a two-course professional program for clinicians, researchers, and health data professionals who want to apply machine learning to real healthcare challenges.

This online, self-paced program covers the essentials of working with healthcare databases, knowledge graphs, and structured and unstructured data, as well as techniques for electronic phenotyping and time series analysis.

You will learn directly from Dr. Nigam Shah, chief data scientist for Stanford Health Care and professor of medicine at Stanford University, and earn a Stanford Certificate of Achievement upon completion.

After completing this program, you will be able to:

Evaluate healthcare databases and the U.S. healthcare data ecosystem, identifying data provenance, sources of error and bias, and strategies to assess and improve data usability.
Build structured clinical datasets from longitudinal timelines by defining units of observation, engineering features, handling missing data, and mitigating data cascades.
Process unstructured clinical text using natural language processing (NLP) and de-identification techniques, and apply knowledge graphs such as the Unified Medical Language System (UMLS) for concept mapping in healthcare data.
Analyze healthcare time-series data, accounting for non-stationarity and the timing of exposures and outcomes to model patient trajectories.
Design and validate electronic phenotypes and reproducible cohorts using rule-based and machine learning approaches, supported by chart review.
Develop, tune, and evaluate supervised learning models, including risk scores and predictive models, using appropriate loss functions, bias–variance tradeoffs, and cross-validation.
Apply deep learning methods to electronic health records (EHR) and medical data, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), attention mechanisms, Transformers, and transfer learning.
Plan and execute clinical AI deployment by defining use cases, understanding steps beyond prediction, ensuring interpretability and generalizability, and addressing ethics and liability.

If you are early in your AI journey, you may want to start with our Artificial Intelligence in Healthcare Specialization.

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Fit Learning into Your Lifestyle

100% Online

Access course content anywhere, anytime

Self-paced

Complete the courses on your schedule

Credentialed

Complete three courses to earn a Certificate of Achievement

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Courses

Data Foundations for Machine Learning in Medicine

Deepen your understanding of data management and processing challenges, and develop the practical data skills required for applying machine learning to complex healthcare datasets.

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Model Development, Deployment, and Ethical Considerations

Build upon the basics of clinical data analysis to explore predictive modeling, model deployment, deep learning, and the ethical and practical considerations of their applications.

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Medical Statistics III

Common Statistical Tests in Medical Research

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Graduate or Professional?

Time Commitment

Achievement

Classmate Interactions

Cost

Graduate Certificate Courses

90–120 hours per course

Earn up to 18 units of academic credit that may contribute to a certificate or a degree

Frequent collaboration with other students taking courses at the same time

$$$$

Professional Certificate Courses

6–13 hours per course

Earn a certificate and, in some cases, professional education units

Potential to connect with other participants through private social media groups

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