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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À propos de ce cours
Résultats de carrière des étudiants
50%
25%
33%
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
Ce que vous allez apprendre
Walk through examples of prognostic tasks
Apply tree-based models to estimate patient survival rates
Navigate practical challenges in medicine like missing data
Compétences que vous acquerrez
Résultats de carrière des étudiants
50%
25%
33%
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
Offert par

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programme du cours : ce que vous apprendrez dans ce cours
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.
Avis
Meilleurs avis pour AI FOR MEDICAL PROGNOSIS
I am a medical image analysis enthusiast. But I always wonder why I can't I combine other patient details for extending it's application. Sure this course is awesome. I really loved it !!
This course was great and more challenging that I have expected. More focus on statistics and survival data which is important for prognosis. Course has a good flow and valuable content.
This course is one of the best courses to learn about Medical Prognosis. Really, the survival models were described in great detail. Thank you, Pranav for this wonderful course.
Def an eye opening course with all the various NLP libraries and also the survival modeling. The content was clear and easy to understand if you have the required background.
À propos du Spécialisation IA pour la médecine
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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