Minimizing Fasting Requirements to Maximize Patient Satisfaction Prior to a Scheduled Cardiac Catheterization

Background: Patients are often kept NPO (nil per os), or “nothing by mouth,” longer than necessary before cardiac catheterization procedures. Local Problem: Patients scheduled for cardiac catheterization procedures receive instructions to begin fasting at midnight on the day of their procedure regardless of their scheduled procedural start time. This directive results in patient dissatisfaction as patients are fasting longer than current guideline recommendation. Methods: The facility updated its order set to align with the American Society of Anesthesiologists (ASA) fasting guidelines for scheduled cardiac catheterizations. To evaluate patient satisfaction, a 6-item survey was administered to 169 individuals undergoing cardiac catheterization. The pre-intervention group (n=116) and postintervention group (n=53) were independent samples. An independent T-test was used to analyze differences in patient satisfaction. Results: A statistically significant difference was observed in both patient satisfaction (p= 0.046) and reported fasting duration (p= 0.0248). The pre-intervention group reported an average fasting burden score of 16.92 and an average fasting time of 12.59 hours, compared to the post-intervention group, which had an average fasting burden score of 15.62 and fasting duration of 11.08 hours. No cases of aspiration were reported with either fasting protocol. Conclusion: The results demonstrate that following ASA guidelines can improve patient satisfaction without increasing the risk of aspiration

Session 7 : \u3cem\u3eUsing Machine Learning to Predict Attrition in a Federal Nutrition Education Program\u3c/em\u3e

Attrition poses a significant challenge to the effectiveness of federal nutrition education programs, hindering their ability to achieve widespread impact. This study employs machine learning techniques to develop a predictive model for identifying participants at high risk of dropping out of the Expanded Food and Nutrition Education Program (EFNEP). Analysis is conducted using standardized EFNEP program data (pre-program 24-hour dietary recalls, food and physical activity questionnaires, and demographic information) on over 1.25 million adult participants from 2013 to 2022. Three machine learning algorithms (logistic regression, XGBoost, and random forest) were evaluated, with the XGBoost model achieving the highest predictive accuracy. Key predictors of attrition included Cooperative Extension region, funding tier, land-grant university type (1860 vs. 1890), enrollment year, household income, age, race, residence, number of children, number of foods consumed in 24-hour dietary recall, and physical activity level. These findings provide valuable insight to EFNEP administrators, enabling them to proactively identify at-risk program participants and implement targeted interventions to improve retention and program impact

White Coats and Scrubs: Bridging the Gaps in Accommodation

Effective communication between nurses and physicians is critical for patient safety, yet these interactions often occur within hierarchical structures that can hinder open dialogue and collaboration. This study explores nurse-physician communication through the lens of Communication Accommodation Theory (CAT), specifically focusing on how nurses accommodate their communication using the framework’s strategies. Drawing on qualitative data from semi-structured interviews with registered nurses, this study identifies key themes including convergence, organizational silence, and maintenance as strategies that nurses engage in. The findings of this study reveal that accommodation choices are not solely motivated by intrinsic goals but can be motivated by external influences such as the context of the interaction and a predetermined motivation

SDSU Data Science Symposium Poster Session, 2025

https://openprairie.sdstate.edu/ds_symposium_2025_poster_gallery/1039/thumbnail.jp

SDSU Data Science Symposium Banquet, 2025

https://openprairie.sdstate.edu/ds_symposium_2025_banquet_gallery/1052/thumbnail.jp

Bayesian Machine Learning Approach for Corn Yield Prediction Using Satellite Imagery and Topographic Data

In an era of climate change and growing global food demand, accurate crop yield prediction is pivotal for leveraging advanced technologies to enhance crop management and sustainability. This study compares the prediction performance of several Bayesian Machine Learning method using high-resolution PlanetScope imagery and topographic data. In specific, the Bayesian Linear Regression, Bayesian Random Forest, Bayesian Splines, Bayesian Additive Regression Trees, and Bayesian Neural Network were developed to incorporate uncertainty quantification and achieve enhanced predictive accuracy. Our finding shows that the Bayesian Random Forest outperform the other model in term of crop yield prediction

Session 2 : \u3cem\u3eCreating a User-friendly Shiny App for Reproducible Two-Sample Mendelian Randomization Studies\u3c/em\u3e

We present a Shiny app that supports and facilitates two-sample Mendelian randomization studies with genome-wide association study (GWAS) summary statistics. The proliferation of GWAS and the sharing of their marginal SNP association statistics have enabled researchers to address causal inference questions between two complex traits. Two-sample Mendelian randomization posits a causal relationship between a putative exposure and a putative outcome. Our Shiny app will enable researchers to input GWAS summary statistics for the putative outcome and putative exposure. The app supports diverse sensitivity analyses to assess the assumptions that underlie Mendelian randomization. To ensure computational reproducibility, the user can download a Rmarkdown file with all analysis code from our app. We also briefly discuss anticipated issues with app deployment

Young: Gertrude Stickney Young Papers

The Gertrude Stickney Young Papers document her significant academic, historical, and civic contributions to South Dakota. A long-time faculty member at South Dakota State University and author of historical works, Young played a pivotal role in shaping the university’s intellectual environment and preserving the state’s history. Key works in the collection include South Dakota: An Appreciation (1944) and Dakota Again (1950), which reflect her dedication to local history. Additionally, her other writings, such as Dr. Frederick A. Stafford in Dakota, 1884-1922 and William Joshua Cleveland, 1845-1910, highlight her broad historical interests. The collection also contains clippings, correspondence, and manuscripts, including personal items and works like Glimpses of South Dakota State College (1957). Other materials document her involvement in local organizations and her collaborations with figures like Ada Caldwell. This collection is a valuable resource for understanding the cultural, educational, and civic history of South Dakota, as well as Young\u27s lasting influence on the state\u27s historical scholarship

2025 SDSU Data Science Symposium Schedule

2025 SDSU Data Science Symposiu