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Built in academia.
Backed by research

Trusted by leading health system

You provide the data, we brings to the science

Together we make healthcare predictable

KenSci's roots in research brings of life Machine Learning models that power predictive insights for leading health systems. Access some of our research here

Death Vs. Data Science: Predicting End of Life

Death is an inevitable part of life and while it cannot be delayed indefinitely it
 is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions.

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Our Researchis at the heart
of everything
we do

KenSci’s risk prediction platform is built on 5+ years of deep industry research that ultimately resulted in a predictive analytics tool for the healthcare industry, focused on saving lives and costs.

Over the years, we have continued and imbibed in our culture, the system of authoring research papers. KenScientists have presented their peer reviewed research at some of the leading conferences across the globe.

With these research papers at the heart of over 180 of KenSci’s Machine Learning Models, specific to healthcare, health systems across the world are optimizing costs and identifying the best care pathways for their patients.

Get access to all our research

Download KenSci’s published papers e-book to gain access to all research papers authored by KenSci and in collaboration with industry experts.

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A Framework to Recommend Interventions for 30-Day Heart Failure Readmission Risk.

In this paper, we describe a novel framework to recommend personalized intervention strategies to minimize 30-day readmission risk for heart failure (HF) patients, as they move through the provider’s cardiac care protocol. We design principled solutions by learning the structure and parameters of a multi- layer hierarchical Bayesian network from underlying high-dimensional patient data. Next, we generate and summarize the rules leading to personalized interventions which can be applied to individual patients as they progress from admit to discharge.

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Meet our researchers

Meet the team that is focussed on KenSci’s research

  • Ankur Teredesai
  • Kiyana Zolfaghar
  • James Marquadt
  • Jasmine Wilkerson
  • Dr. Carly Eckert
  • Muhammad Aurangzeb Ahmad
  • Dr. T Greg McKelvey Jr
  • Vikas Kumar

Research & Insights

Peer reviewed, published research and insights to save more lives.

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