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Thursday, August 22 • 10:30am - 11:00am
A Bayesian Look at Clinical Risk Prediction

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Building reliable predictive and prognostic models that leverage the growing scale of medical data and clinical records can make tremendous impact to the healthcare industry. Traditional survival analysis originated from clinical research focuses on identifying variates and factors that affect the hazard function; time series modelling approaches emphasize predicting future values based on previous observations; machine learning models are often formulated as mapping between large feature spaces to binary outcomes. However, all of these methods have their unique limitations and there are still much more to explore in the context of treating clinical survival analysis with machine learning models. In this talk, we will present our recent work on a new Bayesian framework that uniquely connects machine learning tasks (classification/regression) with event time analysis to provide risk prediction capabilities. We validate and demonstrate the utility of this approach with simulation data where the ground truths are known. We will then show a specific use case of this approach to perform risk prediction with real medical datasets. We will also discuss how this model can be implemented into clinical solutions.

avatar for Kang Liu

Kang Liu

Applied Data Scientist, Wolters Kluwer Health
Dr. Kang Liu graduated with his Ph.D. from Boston University in 2013 and now works as an Applied Data scientist at Wolters Kluwer Health where he builds machine learning models for the early prediction of hospital-acquired infection.

Thursday August 22, 2019 10:30am - 11:00am EDT
Rooms 426 / 428 / 430 595 Commonwealth Avenue, Boston, MA 02215, USA