Selected Recent Videos on Machine Learning in Healthcare

Videos in List: 

4/7 IACS SEMINAR: Building A Machine Learning Health System

Harvard Institute for Applied Computational Science via YouTube:  Presentation by Dr. Nigam Shah, Stanford University.

Video Link: 

Automating Machine Learning for Prevention Research

NIH Office of Disease Prevention via YouTube:  Successful disease prevention will depend on modeling human health as a complex system that is dynamic in time and space and driven by biomolecular and physiologic interactions. The webinar reviews the new discipline of automated machine learning (AutoML), which has the goal of simplifying this process and making machine learning more accessible. Dr. Moore presents an example from human genetics.

Video Link: 

AWS re:Invent 2016: Data Science & Healthcare: Large Scale Analytics & Machine Learning (HLC301)

Amazon Web Services via YouTube: Working with Amazon Web Services “AWS” and 1Strategy, an Advance AWS Consulting partner; the Cambia Health Data Sciences teams have been able to deploy HIPAA compliant and secured AWS Elastic Map Reduce (EMR) data pipelines on the cloud. In this session, we dive deeply into the architectural components of this solution and you will learn how utilizing AWS services has helped Cambia decrease processing time for analytics, increase application flexibility and accelerate speed to production.

Video Link: 

Improving Healthcare With Machine Learning And Advanced Analytics

Microsoft Power via YouTube:  This session will focus on real use cases of how Advanced Analytic solutions from Microsoft are being applied today to: predict the impact of weather patterns on health service utilization in Norway (Azure ML) and improve the health of people with Chronic Disease conditions in the United States by aggregating Electronic Medical Records (EMR) data with open data sources to spot previously unrecognized factors contributing to poor health (SQL, Power Query, Power Map, Power BI).

Video Link: 

Rich Caruana: Intelligible Machine Learning Models for HealthCare

Allen Institute for Artificial Intelligence (AI2) via YouTube: In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible (e.g., random forests, boosted trees, and neural nets), and the most intelligible models usually are less accurate (e.g., linear or logistic regression). This tradeoff sometimes limits the accuracy of models that can be applied in mission-critical applications such as healthcare where being able to understand, validate, edit, and trust a learned model is important.

Video Link: 

Trending Videos over past 30 days

  • Past:
  • 1 month