Daniel R. Cavazos
Papers
1
Total Citations
16
H-Index
1
About
Daniel R. Cavazos is a researcher at the forefront of applying machine learning to orthopedic surgery, with a particular focus on improving outcomes in total knee arthroplasty (TKA). His most-cited work, "Predicting Factors for Blood Transfusion in Primary Total Knee Arthroplasty Using a Machine Learning Method" (2023, 16 citations), exemplifies his innovative approach to a persistent clinical challenge: acute blood loss anemia requiring allogeneic transfusion after TKA. By developing and validating machine learning models, Cavazos not only identifies the key contributing factors to transfusion risk but also provides surgeons with a practical, data-driven tool to anticipate and mitigate this complication. This work bridges the gap between advanced computational methods and everyday clinical decision-making, offering the potential to enhance patient safety, reduce healthcare costs, and optimize resource allocation. His research stands as a compelling model for how predictive analytics can transform perioperative care, making him a notable voice in the growing field of AI-assisted orthopedics.
Research Focus
Key Achievements
Top Papers
- 1