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Our Research

Presented at leading forums around the globe

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Read some of the extensive work that KenSci has pioneered through the course of our journey

Automatic Detection of Excess Healthcare Spending and Cost Variation in ACOs

Liu E, Ahmad MA, Eckert C, Nascimento A, De Cock M, Padthe K, Teredesai A, McKelvey G.

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Machine Learning Models for Surgical Site Infection Prediction

Mandagani, P., Coleman, S., Zahid, A., Ehlers, A.P., Roy, S.B. and De Cock, M.

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GUIdock-VNC: Using a Graphical Desktop Sharing System to Provide A Browser-Based Interface…

Mittal V, Hung LH, Keswani J, Kristiyanto D, Lee SB, Yeung KY

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Impact of A Mobile Health Application on User Engagement and Pregnancy Outcomes Among…

James Bush, MD, FACP, Dilek E. Barlow, MA, Jennie Echols, PhD, MSN, RN, Jasmine Wilkerson, MS, and Katherine Bellevin, MA

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Adaptive City Characteristics: How Location Familiarity Changes What Is…

Vikas Kumar, Saeideh Bakhshi, Lyndon Kennedy, David A. Shamma.

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The Force Within: Recommendations Via Gravitational Attraction Between Items

Vikas Kumar, Saeideh Bakhshi, Lyndon Kennedy, David A. Shamma.

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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.

Ahmad, M. A., Eckert, C., McKelvey, G., Zolfaghar, K., Zahid, A., Teredesai, A.

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Readmissions Score as a Service (RaaS)

Vivek R Rao; Kiyana Zolfaghar; David K. Hazel; Vani Mandava; Senjuti Basu Roy; Ankur Teredesai;

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Risk-O-Meter_An Intelligent Clinical Risk Calculator

Kiyana Zolfaghar; Jayshree Agarwal; Deepthi Sistla; Si-Chi Chin; Senjuti Basu Roy; Nele Verbiest

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The Challenge of Imputation in Explainable Artificial Intelligence

Muhammad Aurangzeb Ahmad; Carly Eckert M.D; Ankur Teredesai;

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Dietary intake assessment using integrated sensors

Junqing Shang; Eric Pepina; Eric Johnsonb;David Hazel; Ankur Teredesai; Alan Kristal; Alexander Mamisheva

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

Rui Liu, Kiyana Zolfaghar, Si-chi Chin, Senjuti Basu Roy, Ankur Teredesai

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Population Cost Prediction on Public Healthcare Datasets

Shanu Sushmita; Stacey Newman; James Marquardt; Prabhu Ram; Virendra Prasad; Martine De Cock; Ankur Teredesai;

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Interpretable Machine Learning in Healthcare

Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur Teredesai, and Greg McKelvey

The drive towards greater penetration of machine learning in healthcare is being accompanied by increased calls for machine learning and AI based systems to be regulated and held accountable in healthcare. Interpretable machine learning models can be instrumental in holding machine learning systems accountable. Healthcare offers unique challenges for machine learning where the demands for explainability, model fidelity and performance in general are much higher as compared to most other domains. In this paper we review the notion of interpretability within the context of healthcare, the various nuances associated with it, challenges related to interpretability which are unique to healthcare and the future of interpretability in healthcare

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Our Research is 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.

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