Qing Bao
Papers
1
Total Citations
3
H-Index
1
About
Qing Bao is a researcher whose work sits at the intersection of neurorehabilitation and biomedical engineering, with a particular focus on developing predictive tools for robotic-assisted therapy. Their most-cited study, "Predictive nomogram for soft robotic hand rehabilitation of patients with intracerebral hemorrhage" (2022), addresses a critical gap in personalized medicine for stroke recovery. By establishing a nomogram that identifies risk factors influencing hand rehabilitation outcomes during soft robotic hand therapy (SRHT), Bao's work provides clinicians with a practical, data-driven method to tailor treatment plans for intracerebral hemorrhage patients. This contribution is especially valuable given the growing use of soft robotics in rehabilitation, where patient-specific factors often determine success. While their citation count is currently modest, the study's methodological rigor and clinical relevance position it as a foundational piece for future research in predictive rehabilitation models. Bao's work exemplifies the integration of statistical modeling with cutting-edge therapeutic technology, offering a pathway toward more precise and effective neurorehabilitation strategies.
Research Focus
Key Achievements
Top Papers
- 1