AI for Medical Prognosis

DeepLearning.AI via Coursera

Coursera based on 786 ratings

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Overview

AI is transforming the practice of medicine. Itsing 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. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, youll walk through multiple examples of prognostic tasks. Youll then use decision trees to model non-linear...

Syllabus

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 patients relative risk of getting a particular disease. You will then evaluate each models performance by implementing and using a concordance index that incorporates time to event and censored data.