05-02, 13:30–13:55 (Asia/Jerusalem), PyData Track 1
In this talk, you will learn how at Diagnostic Robotics we create insights from claims data, a form of administrative data at large scale, which provides a great opportunity for AI in healthcare. You will understand how we use medical code embeddings
In this talk, you will learn how at Diagnostic Robotics we create insights from claims data, a form of administrative data at large scale, which provides a great opportunity for AI in healthcare. You will understand how we use medical code embeddings and deep learning methods to build predictive proactive models that benefit the patients and reduce the cost of healthcare. We will also discuss the concept of causal machine learning, its use to emulate randomised controlled trials and see how it’s related to our models.
English
Target audience –Data Scientists
Noa is a Machine Learning Researcher at Diagnostic Robotics. She previously worked at Amazon, NASA, Elbit Systems, and the Israeli Aerospace Industry. Noa has an MSc in Computer Science from Bar-Ilan University (Magna Cum Laude) with an NLP thesis advised by Prof. Yoav Goldberg. Her Electrical Engineering BSc is from the Technion (Summa Cum Laude).