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NLP aspects in medical records- from visit texts to medical concepts matrix

In this talk, we present our ongoing work utilizing more than 60 billion historical medical visits to create an automated layer for digital healthcare.

We will discuss the NLP challenges working with medical summaries in Hebrew.

We will present our Auto tagging ML model for automated entities extraction from medical summaries.

Our pipeline includes novelty deep models architectures built from scratch for sentence splitting, negation detection, entities relations and terms expansions.

We will share from our insights discovered from applying those systems in practice.

AI in Healthcare – Promise and Perils

There has been an explosion of applications of AI in healthcare. And they promise to deliver a whole range of capabilities affecting...