Machine Learning in Healthcare: Techniques and Applications
summary
Applications of Gradient Boosting, Deep Learning, and Unsupervised Learning to healthcare problems are presented, with concrete examples including predicting complications in an ICU and exploring new evidence about Alzheimer’s disease mechanisms. The talk also covers practical challenges such as working with small datasets and addresses ethical and privacy concerns in applying machine learning in clinical and hospital management contexts.
AI-written summary · about
chapters (7)
- 0:00Start
- 6:29Big Data
- 7:29Little Data
- 18:30Disease diagnosis at scale: Alzheimer
- 20:29Training process
- 34:45Privacy
- 42:44Audit automation
related talks
20mMachine Learning in Cardiology
58mHealthcare with AI of the Future
22mLeveraging AI/ML for Transformative Software Solutions in Healthcare
33mData Science in Healthcare: Beyond the Hype
49mUsing Machine Learning and Not Dying Trying
18mMedical AI Beyond Prediction: From Normative Learning to Agentic Healthcare AI
47mMachine Learning for medical rehabilitation
25mDeep Learning in Biomedicine: Challenges and Opportunities