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<p>Clinical prediction models offer the ability to help physicians make better data-driven
decisions that can improve patient outcomes. Given the wealth of data available with
the widespread adoption of electronic health records, more flexible statistical models
are required that can account for the messiness and complexity of this data. In this
dissertation we focus on developing models for clinical time series, as most data
within healthcare is collected longitudinally and it is important to take this structure
into account. Models built off of Gaussian processes are natural in this setting of
irregularly sampled, noisy time series with many missing values. In addition, they
have the added benefit of accounting for and quantifying uncertainty, which can be
extremely useful in medical decision making. In this dissertation, we develop new
Gaussian process-based models for medical time series along with associated algorithms
for efficient inference on large-scale electronic health records data. We apply these
models to several real healthcare applications, using local data obtained from the
Duke University healthcare system.</p><p>In Chapter 1 we give a brief overview of
clinical prediction models, electronic health records, and Gaussian processes. In
Chapter 2, we develop several Gaussian process models for clinical time series in
the context of chronic kidney disease management. We show how our proposed joint model
for longitudinal and time-to-event data and model for multivariate time series can
make accurate predictions about a patient's future disease trajectory. In Chapter
3, we combine multi-output Gaussian processes with a downstream black-box deep recurrent
neural network model from deep learning. We apply this modeling framework to clinical
time series to improve early detection of sepsis among patients in the hospital, and
show that the Gaussian process preprocessing layer both allows for uncertainty quantification
and acts as a form of data augmentation to reduce overfitting. In Chapter 4, we again
use multi-output Gaussian processes as a preprocessing layer in model-free deep reinforcement
learning. Here the goal is to learn optimal treatments for sepsis given clinical time
series and historical treatment decisions taken by clinicians, and we show that the
Gaussian process preprocessing layer and use of a recurrent architecture offers improvements
over standard deep reinforcement learning methods. We conclude in Chapter 5 with a
summary of future areas for work, and a discussion on practical considerations and
challenges involved in deploying machine learning models into actual clinical practice.</p>
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