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.
提供:


このコースについて
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
学習内容
Walk through examples of prognostic tasks
Apply tree-based models to estimate patient survival rates
Navigate practical challenges in medicine like missing data
習得するスキル
- Deep Learning
- Machine Learning
- time-to-event modeling
- Random Forest
- model tuning
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
提供:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
シラバス - 本コースの学習内容
Linear prognostic models
Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.
Prognosis with Tree-based models
Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.
Survival Models and Time
This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5 year, 7 year, or 10 year risk.
Build a risk model using linear and tree-based models
This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.
レビュー
- 5 stars77.88%
- 4 stars16.54%
- 3 stars3.28%
- 2 stars1.56%
- 1 star0.71%
AI FOR MEDICAL PROGNOSIS からの人気レビュー
Excellent course! Real world data and robust models. Of particular value was the implementation of the SHAP feature interpretation algorithm as applied to ensemble models.
The course is amazing. And practical quizzes and assignments are just perfect and up-to date with current affairs.
after taking Deep Learning Specialization and machine learning and python on coursera things are getting better and clearer in this course.
Nicely and very thought fully organized course. This will definitely help me in apply my skills at my job. Thanks DeepLEarning.ai
医学のためのAI専門講座について
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 three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.

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