Devaluing energy-dense foods for cancer control

Fri, 24 Apr 2020 00:00:00 +0000

This project is a large-scale randomized-control trial that investigates the efficacy and mechanisms of a healthy eating invention. Specifically, the RCT compares a cognitive reappraisal training, in which participants change the way they think about unhealthy food, to a behavioral response training, in which participants modify their physical motor responses to food stimuli to train neural inhibitory control circuits. Currently, I am working on developing a precision medicine analysis pipeline around a series of additional measures that have been added to this parent R01 as part of the National Center for Biotechnology Information’s ADOPT Project (Accumulating Data to Optimally Predict Obesity Treatment). The ADOPT project aims to solve the precision medicine problem for obesity treatment by identifying core measures assessing a range of behavioral, biological, environmental, and psychosocial factors that contribute to obesity. My role in this project is to develop the analytical infrastructure to create and validate composites of low-cost, easily administered individual difference measures that moderate response to the healthy eating interventions in terms of both magnitude and timing.

Predicting diabetes status from personality

Fri, 24 Apr 2020 00:00:00 +0000

Prior research suggests that diabetes is both a cause and consequence of various health behaviors and thus has complex links to personality. This study investigates the utility of personality in predicting diabetes status (Type 1, Type 2 or none) using a subset of items from the Synthetic Aperture Personality Assessment (SAPA) Project (N ≈ 645,000). We estimate the classification accuracy of various personality measures, including low- and high-dimensional data, using supervised machine learning techniques, e.g. random forests, support vector machines and neural networks. Results are compared across machine learning models and dimensionality of personality predictors. This project involves the automated generation of executable R scripts using non-standard evaluation within a tidyverse framework, allowing for parallel processing on high-performance computing clusters. This analysis informs the extent to which personality measurement could be applied in clinical contexts related to diabetes.

machine learning | Waseem Ashfaq

Devaluing energy-dense foods for cancer control

Predicting diabetes status from personality