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Browsing by Subject "Bayesian networks"

Now showing 1 - 2 of 2
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    Cardiovascular risk prediction from Electronic Health Records using probabilistic graphical models.
    (2016-06) Bandyopadhyay, Sunayan
    Cardiovascular (CV) disease is one of the leading causes of death in the United States; therefore, it is of vital importance that it be managed and treated effectively. Such treatment requires information to determine optimal strategies for treating complex patients so as to minimize their risk of a CV event. Creating such information requires the availability of predictive models that can estimate the probability of a CV event occurring over a fixed time horizon. Currently available predictive models are limited because they are constructed from carefully curated cohorts which may not be representative of the population currently under care. This limitation can largely be overcome by using more representative data. Electronic health records (EHR) provide us with such observations which are representative of the population currently being treated by physicians. They provide an attractive platform over which we can construct a predictive model. However, EHR data may have weaknesses, which include missing data and incomplete follow-up. As a result, it is not possible to apply unmodified traditional machine learning algorithms for constructing a predictive model. In this thesis we show how to adapt probabilistic graphical models (PGMs) to censored data with missing observations. In addition, we construct variants of adapted PGMs that allow us to take advantage of different types of historical observations available in the EHR to better predict the risk of CV events.
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    A new approach to lattice quantum field theories
    (2018-01) Jain, Mudit
    In this thesis report, I describe an algorithm for lattice simulation of quantum/statistical fields that reduces the complexity of current techniques (Metropolis algorithm) from exponential in all the directions of space and (Euclidean-)time, to linear in (Euclidean-)time and exponential in space. This is done by building a typical field configuration spatial slice by spatial slice through an analytically obtained Markov chain from its path integral. Although the complexity still depends exponentially on the number of spatial lattice points, for quantum mechanics ($0+1$ fields) spatial slice is only a point and thus the complexity only depends linearly on the number of time lattice points and simulation becomes pretty easy. As examples, I discuss the cases of harmonic and an-harmonic oscillators along with some simulation results. The case of Gaussian fields in general (in any dimension) is trivial since in the similarity transformed space each lattice site decouples and hence there exists a random variable at each lattice site that does not interact with any other. Although the reduction of complexity from exponential in space (if possible) for higher dimensional fields in general is currently under investigation, I present a checkerboard network that we investigated along with some simulation results.