Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques
We integrated predictive modeling and data mining to characterize patients adversely affected by prescription stimulant use. Future research is needed to externally validate identified features to guide safer stimulant prescribing.
Authors
- Curtis Bone (ORCID: https://orcid.org/0000-0002-9872-2145)
- Wen‐Jan Tuan (ORCID: https://orcid.org/0000-0003-3939-8979)
- Paul M. Griffin (ORCID: https://orcid.org/0000-0002-0206-4775)
- Yong Yan (ORCID: https://orcid.org/0000-0001-7135-5456)
- Rafay Nasir (ORCID: https://orcid.org/0009-0004-1761-8605)
- Qiushi Chen (ORCID: https://orcid.org/0000-0003-4031-2669)
Institutions
- Pennsylvania State University (US)
- The University of Texas at San Antonio Health Science Center (US)
- Penn State Milton S. Hershey Medical Center (US)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.109870
- Primary Topic
- Blood Pressure and Hypertension Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Institute for Computational and Data Sciences, Pennsylvania State University
- National Center for Advancing Translational Sciences