Yael
Steuerman

Utilizing Patient-Specific Longitudinal Data to Predict Future Events
K-Health
Yael Steuerman

Yael
Steuerman

Utilizing Patient-Specific Longitudinal Data to Predict Future Events

K-Health
Yael Steuerman

Bio

Yael is an AI researcher at K-Health, where she utilizes AI methods to provide data-driven personalized health-care for consumers.


She completed her PhD in Computational Biology at Tel-Aviv University, where she studied the immune response to Influenza infection using machine learning tools. She has a passion for data and seeks to develop computational tools to explore biomedical questions.


Yael also co-founded ‘Code Goddess’ (Project Mehamamet), a non-profit initiative aiming to increase the participation of girls in STEM fields.

Bio

Yael is an AI researcher at K-Health, where she utilizes AI methods to provide data-driven personalized health-care for consumers.

 

She completed her PhD in Computational Biology at Tel-Aviv University, where she studied the immune response to Influenza infection using machine learning tools. She has a passion for data and seeks to develop computational tools to explore biomedical questions.

 

Yael also co-founded ‘Code Goddess’ (Project Mehamamet), a non-profit initiative aiming to increase the participation of girls in STEM fields.

Abstract

Electronic medical records are a rich source of longitudinal, individualized medical data. The wide use of these resources allowed the development of deep learning methods to better understand disease progression and predict disease onset. While these methods offer impressive accuracy they lack in interpretability, since it is often hard to tell why a specific person is predicted to develop a disease.

 

At K, we strive to develop data-driven interpretable methods that will help us understand their medical predictions. Here, I will present how we exploit longitudinal patient-specific data and deep learning methodologies to predict the onset of Ischemic Heart disease. I will further focus on how these temporal events can be used to provide medical insights at both the individual and population levels.

Abstract

Electronic medical records are a rich source of longitudinal, individualized medical data. The wide use of these resources allowed the development of deep learning methods to better understand disease progression and predict disease onset. While these methods offer impressive accuracy they lack in interpretability, since it is often hard to tell why a specific person is predicted to develop a disease.

 

At K, we strive to develop data-driven interpretable methods that will help us understand their medical predictions. Here, I will present how we exploit longitudinal patient-specific data and deep learning methodologies to predict the onset of Ischemic Heart disease. I will further focus on how these temporal events can be used to provide medical insights at both the individual and population levels.

Planned Agenda

Planned Agenda