Prediction of Survival Analysis for Cancer Patients
Offered By: International Centre for Theoretical Sciences via YouTube
Course Description
Overview
Explore survival analysis prediction for cancer patients in this comprehensive lecture from the "Machine Learning for Health and Disease" program. Delve into advanced techniques for leveraging clinical and lifestyle parameters to forecast patient outcomes. Learn how to apply various machine learning methods, including logistic regression, tree-based algorithms, support vector machines, Bayesian approaches, and deep networks, to biomedical and health-related data. Gain insights into analyzing diverse patient data such as X-rays, ultrasound images, and ECG measurements, as well as genomic variant analysis and pattern inference in large-scale heterogeneous datasets. Bridge the gap between mathematical modeling and clinical problems while discovering tools that can be easily adapted to analyze healthcare data.
Syllabus
Prediction of Survival Analysis for Cancer Patients Taking Into... by Shakuntala Baichoo
Taught by
International Centre for Theoretical Sciences
Related Courses
CERTaIN: Pragmatic Clinical Trials and Healthcare Delivery EvaluationsThe University of Texas MD Anderson Cancer Center via edX Advanced Machine Learning
Higher School of Economics via Coursera Interpreting Data with Advanced Statistical Models
Pluralsight Recommender Systems and Deep Learning in Python
Udemy Advanced Bayesian Methods
The National Centre for Research Methods via YouTube