AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
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Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.
These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.
Course 3 of 3 in the AI for Medicine Specialization.
What You Will Learn
- Estimate treatment effects using data from randomized control trials
- Explore methods to interpret diagnostic and prognostic models
- Apply natural language processing to extract information from unstructured medical data
Syllabus
WEEK 1
Treatment Effect Estimation
In this week, you will learn: How to analyze data from a randomized control trial, interpreting multivariate models, evaluating treatment effect models, and interpreting ML models for treatment effect estimation.
WEEK 2
Medical Question Answering
In this week, you will learn how to extract disease labels from clinical reports, and also question answering with BERT.
WEEK 3
ML Interpretation
In this week, you will learn how to interpret deep learning models, and also feature importance in machine learning.
