Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical time series data. However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological...
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Zusammenfassung: | Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric
framework that has proven useful in jointly modeling the physiological states
of patients in medical time series data. However, capturing the short-term
effects of drugs and therapeutic interventions on patient physiological state
remains challenging. We propose a novel approach that models the effect of
interventions as a hybrid Gaussian process composed of a GP capturing patient
physiology convolved with a latent force model capturing effects of treatments
on specific physiological features. This convolution of a multi-output GP with
a GP including a causal time-marked kernel leads to a well-characterized model
of the patients' physiological state responding to interventions. We show that
our model leads to analytically tractable cross-covariance functions, allowing
scalable inference. Our hierarchical model includes estimates of
patient-specific effects but allows sharing of support across patients. Our
approach achieves competitive predictive performance on challenging hospital
data, where we recover patient-specific response to the administration of three
common drugs: one antihypertensive drug and two anticoagulants. |
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DOI: | 10.48550/arxiv.1906.00226 |