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A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics
William Hoiles · Mihaela van der Schaar

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #119

Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital.

Author Information

William Hoiles (University of California)
Mihaela van der Schaar (University of Cambridge)

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