DeepLearning.AI - AI For Medical Treatment
- Offered byCoursera
AI For Medical Treatment at Coursera Overview
Duration | 22 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
AI For Medical Treatment at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 3 in the AI for Medicine Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level We recommend first completing Course 1 and 2 of the AI For Medicine Specialization.
- Approx. 22 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
AI For Medical Treatment at Coursera Course details
- 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.
- 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.
AI For Medical Treatment at Coursera Curriculum
Treatment Effect Estimation
Intro to Course 3 with Andrew and Pranav
About Course 3
Absolute risk reduction
Randomized control trials
Causal inference
Average treatment effect
Conditional average treatment effect
T-Learner
S-Learner
Evaluate individualized treatment effect
C-for-benefit
C-for-benefit calculation
Connect with your mentors and fellow learners on Slack
About the automatic grader
How to refresh your workspace
Quiz: Measuring Treatment Effects
Medical Question Answering
Medical question answering
Handling words with multiple meanings
Define the answer in a text
Automatic label extraction for medical imaging
Synonyms for labels
Is-a relationships for labels
Presence or absence of a disease
Evaluating label extraction
Precision and recall and F1 score
Evaluating on multiple disease categories
Quiz: Information Extraction with NLP
ML Interpretation
Drop column method
Permutation method
Individual feature importance
Shapley values
Combining importances
Shapley values for all patients
Interpreting CNN models
Localization maps
Heat maps
Acknowledgements
Citations
ML Interpretation
AI For Medical Treatment at Coursera Admission Process
Important Dates
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