Data Mining–Based Analysis and Reflections on Hospital Ethical Review of Clinical Research Projects

Clinical research projects data that were reviewed by the ethics committee of the authors' institution and registered on an online public platform from January 2019 to December 2021 were collected. High-frequency words were identified using the jieba word segmentation and word cloud visualization of Python 3.11.5 to analyze the key points of ethical review and propose reflections. A total of 227 clinical research projects data were collected. High-frequency words were clustered into four categories: disease types, drug applications, protocol design, and participant selection. Based on these findings, four areas for improvement were proposed: strengthening valuation of clinical research on tumors, ensuring rational drug applications, improving the scientific rigor of study design, and protecting the rights and safety of participants. Utilizing data mining techniques to analyze and reflect on clinical research projects data undergoing hospital ethical review can provide key directional cues and decision-making grounds for improving the quality and efficiency of precise ethical reviews in hospitals.