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 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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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.
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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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.
View PDFMeet the team that is focussed on KenSci’s research
Ankur Teredesai
Kiyana Zolfaghar
James Marquadt
Jasmine Wilkerson
Dr. Carly Eckert
Muhammad Aurangzeb Ahmed
Dr. T Greg McKelvey Jr
Vikas Kumar
