About

Prince Afriyie is an Associate Professor of Data Science at the School of Data Science. His research interests include Theory, Foundations, and Advanced Methodologies, with a focus on Artificial Intelligence and Machine Learning. He serves as the program director of the M.S. in Data Science residential program. His educational background is in mathematics and statistics, and his work involves advancing foundational aspects of data science and AI.

Research topics

• Biology
• Horticulture
• Medicine
• Soil science
• Ecology
• Internal medicine
• Surgery
• Cardiology
• Agronomy
• Environmental science
• Botany

Selected publications

• Predictive Models and Predictors of Under-5 Mortality Using Machine Learning Techniques: Evidence from the 2022 DHS

  Research Square · 2025-11-26

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• An Analysis of Behavior Goals in Individualized Education Programs of Students With Extensive Support Needs

  The Journal of Special Education · 2025-01-05 · 2 citations

  articleSenior author

  Students with extensive support needs (ESN) have an increased risk of engaging in challenging behavior due to a range of factors, including communication and health needs common among this student population. When students engage in behaviors that impede learning, school teams organize support across social and emotional domains to ensure access to free and appropriate public education (FAPE) as outlined in the U.S. Individuals with Disabilities Education Improvement Act. These supports are often formalized in the student’s Individualized Education Program (IEP). This analysis aimed to explore the type and proportion of behavior goals in the IEPs of 1,103 students with ESN across four school districts in the Mountain West region of the United States. We found that students with ESN served in traditional schools were significantly more likely to have a larger proportion of behavior goals related to social skills and emotional regulation than students with ESN served in separate schools. Students with ESN served in separate schools were significantly more likely to have a greater proportion of behavior goals related to compliance and on-task behavior than students with ESN served in traditional schools. We discuss implications for future research and practice related to IEP goal development for students with ESN.

  PublisherDOI

• Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning

  American Heart Journal · 2024-02-07 · 3 citations

  articleOpen access
  PublisherOA PDFDOI

• CARNA: Characterizing Advanced heart failure Risk and hemodyNAmic phenotypes using learned multi-valued decision diagrams

  arXiv (Cornell University) · 2023-06-11 · 1 citations

  preprintOpen access

  Early identification of high risk heart failure (HF) patients is key to timely allocation of life-saving therapies. Hemodynamic assessments can facilitate risk stratification and enhance understanding of HF trajectories. However, risk assessment for HF is a complex, multi-faceted decision-making process that can be challenging. Previous risk models for HF do not integrate invasive hemodynamics or support missing data, and use statistical methods prone to bias or machine learning methods that are not interpretable. To address these limitations, this paper presents CARNA, a hemodynamic risk stratification and phenotyping framework for advanced HF that takes advantage of the explainability and expressivity of machine learned Multi-Valued Decision Diagrams (MVDDs). This interpretable framework learns risk scores that predict the probability of patient outcomes, and outputs descriptive patient phenotypes (sets of features and thresholds) that characterize each predicted risk score. CARNA incorporates invasive hemodynamics and can make predictions on missing data. The CARNA models were trained and validated using a total of five advanced HF patient cohorts collected from previous trials, and compared with six established HF risk scores and three traditional ML risk models. CARNA provides robust risk stratification, outperforming all previous benchmarks. Although focused on advanced HF, the CARNA framework is general purpose and can be used to learn risk stratifications for other diseases and medical applications.

  PublisherOA PDFDOI

• Right atrial structural remodeling predict worse outcomes in transcatheter mitral valve repair

  Catheterization and Cardiovascular Interventions · 2022 · 2 citations

  • Medicine
  • Internal medicine
  • Cardiology

  BACKGROUND: In the current study, we assess the predictive role of right and left atrial volume indices (RAVI and LAVI) as well as the ratio of RAVI/LAVI (RLR) on mortality following transcatheter mitral valve repair (TMVr). METHODS: Transthoracic echocardiograms of 158 patients who underwent TMVr at a single academic medical center from 2011 to 2018 were reviewed retrospectively. RAVI and LAVI were calculated using Simpson's method. Patients were stratified based on etiology of mitral regurgitation (MR). Cox proportional-hazard regression was created utilizing MR type, STS-score, and RLR to assess the independent association of RLR with survival. Kaplan-Meier analysis was used to analyze the association between RAVI and LAVI with all-cause mortality. Hemodynamic values from preprocedural right heart catheterization were also compared between RLR groups. RESULTS: Among 123 patients included (median age 81.3 years; 52.5% female) there were 50 deaths during median follow-up of 3.0 years. Patients with a high RAVI and low LAVI had significantly higher all-cause mortality while patients with high LAVI and low RAVI had significantly improved all-cause mortality compared to other groups (p = 0.0032). RLR was significantly associated with mortality in patients with both functional and degenerative MR (p = 0.0038). Finally, Cox proportion-hazard modeling demonstrated that an elevated RLR above the median value was an independent predictor of all-cause mortality [HR = 2.304; 95% CI = 1.26-4.21, p = 0.006] when MR type and STS score were accounted for. CONCLUSION: Patients with a high RAVI and low LAVI had significantly increased mortality than other groups following TMVr suggesting RA remodeling may predict worse outcomes following the procedure. Concordantly, RLR was predictive of mortality independent of MR type and preprocedural STS-score. These indices may provide additional risk stratification in patients undergoing evaluation for TMVr.

  PublisherOA PDFDOI

• The Effect of Spur Position and Pruning Severity on Shoot Development

  Agronomy · 2022 · 1 citations

  • Horticulture
  • Biology
  • Botany

  Adjusting yearly pruning severity is a common vineyard management practice employed to manipulate vegetative and reproductive growth in grapevines. Although the effects of pruning on total vegetative growth are well documented, there is little research on the effects of adjusting shoots meter−1 via dormant season pruning on addressing mid-cordon shoot weakness and developmental delays. Cordon-trained, spur-pruned vines are thought, by many growers, to be especially prone to weaker positions and delayed development at mid-cordon positions. This phenomenon is also thought to become more exaggerated as the vine ages. Therefore, the effects of shoot density manipulation, implemented via dormant pruning practices, to homogenize shoot and cluster development along the length of the cordon were examined. In this research, Cabernet Sauvignon grapevines were pruned to either 5.5 shoots meter−1 (5.5) or 11.1 shoots meter−1 (11.1). To control for variations in light interception into the fruiting zone, a control of 11.1 shoots meter−1 with sensor guided leaf thinning (11.1LT) was implemented at full berry set to match the canopy light of the 5.5 shoots meter−1 treatment. It was found that individual shoot growth and yield were directly impacted by manipulation of pruning severity. Shoot growth response varied primarily by growing season, including shoot length and internode length. Yield components were significantly lower in the 5.5 treatment during the first two years of the study but were not significantly different during the last year of the study. The 5.5 treatment resulted in the highest pH and total soluble solids at harvest in 2016 and 2017.

  PublisherOA PDFDOI

• Soil management induced shifts in nematode food webs within a Mediterranean vineyard in the Central Coast of California (USA)

  Applied Soil Ecology · 2020 · 13 citations

  • Environmental science
  • Agronomy
  • Biology
  PublisherDOI

• Interview With Prince Afriyie: From Ghana to America

  Journal of Statistics Education · 2020-05-03

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Using Mobile Eye Tracking and Coefficient K for Analysing Usability Trials

  2019-01-01

  articleSenior author
  Publisher

Frequent coauthors

• Sula Mazimba

  AdventHealth Orlando

  4 shared

• Kenneth C. Bilchick

  University of Illinois Chicago

  3 shared

• Allan J. Rossman

  California Polytechnic State University

  2 shared

• Jean C. Dodson Peterson

  Washington State University

  2 shared

• Steven Lamp

  University of Virginia

  2 shared

• Nicholas Ashur

  University of Virginia

  2 shared

• Khadijah Breathett

  Indiana University – Purdue University Indianapolis

  2 shared

• Feng Lu

  Zhejiang Center for Disease Control and Prevention

  2 shared