KenSci CTO Ankur Teredesai is exploring with machine learning and artificial intelligence the dynamics in the healthcare system at the end of an individual’s life. By Larry Dignan

for Between the Lines

| February 27, 2018 — 11:00 GMT (03:00 PST)

| Topic: Artificial Intelligence Video: How KenSci uses machine learning and AI to predict end of life KenSci, a company that has developed a machine learning risk prediction platform for healthcare, recently presented a paper on predicting end-of-life mortality and improving care.

The paper, which tackles a tricky topic with predictions for the last six to 12 months of life for patients, was accepted by the Association for the Advancement of Artificial Intelligence. At stake is $205 billion in cost spent on care for the last year of an individual’s life.

But it’s not just about costs. Here’s an excerpt from the paper Death vs. Data Science: Predicting End of Life.

As part of our ongoing series on data scientists and their approaches, we caught up with Ankur Teredesai, CTO of KenSci and one of the authors of the paper, which was recognized in the emerging technologies category. The challenge of predicting 6-12 month mortality risk is fairly complex.

It’s a $205 billion problem just in the U.S. At KenSci we have a platform that is designed for scale and operational effectiveness of machine learning to solve societal problems such as these with such a large impact. In this particular setting, we had existing machine learning models for 6-12 month mortality prediction from prior efforts. Read more from…

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