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.
AI For Medical Treatment

AI For Medical Treatment
This course is part of AI for Medicine Specialization



Instructors: Pranav Rajpurkar
Access provided by Xavier School of Management, XLRI
28,900 already enrolled
532 reviews
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What you'll 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
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There are 3 modules in this course
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.
What's included
12 videos4 readings1 assignment1 programming assignment3 ungraded labs
In this week, you will learn how to extract disease labels from clinical reports, and also question answering with BERT.
What's included
10 videos1 assignment1 programming assignment3 ungraded labs
In this week, you will learn how to interpret deep learning models, and also feature importance in machine learning.
What's included
9 videos4 readings1 assignment1 programming assignment3 ungraded labs
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Reviewed on Jun 23, 2020
A bit tough, but well laid and well explained.Overall the entire specialization was very good. However it misses in depth theory . But overall a very good course with practical applications
Reviewed on Dec 7, 2020
The assignment for the first week was out of scope for the course in my opinion. It was too much focused on a good handling of pandas which is rather difficult for people who are not experts in pandas
Reviewed on Jun 5, 2020
Building a treatment model and evaluation, take this course to fully understand what to consider. A practical Model for Mediacl Treament
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