Publications by authors named "C Fonnesbeck"
PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU.
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- Adolescents with type 1 diabetes (T1D) face difficulties in managing their condition due to various psychosocial and contextual factors that are hard to assess using traditional methods.
- The study aims to create a machine learning algorithm to predict missed self-management tasks, specifically focusing on mealtime self-monitoring of blood glucose and insulin administration.
- Data from a pilot study was analyzed, combining ecological momentary assessment from a mobile app with blood glucose data to develop classifiers that could forecast self-management behaviors based on contextual and time-related factors.
Background: Acute gastroenteritis (AGE) is a common reason for children to receive medical care. However, the viral etiology of AGE illness is not well described in the post-rotavirus vaccine era, particularly in the outpatient (OP) setting.
Methods: Between 2012 and 2015, children 15 days through 17 years old presenting to Vanderbilt Children's Hospital, Nashville, Tennessee, with AGE were enrolled prospectively from the inpatient, emergency department, and OP settings, and stool specimens were collected.
A nonparametric updating method to correct clinical prediction model drift.
J Am Med Inform Assoc
December 2019
Objective: Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models.
Materials And Methods: We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy.