Recently, Wiley, the internationally renowned publishing group, officially announced the winners of the "2025 Q2 Wiley China Highly Cited Author Award".
A paper titled "Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers Based on the Braden Scale: A Clinical Study Based on MIMIC‐IV", which was published in the Journal of Clinical Nursing, has garnered widespread attention from scholars worldwide immediately after its release. With Chen Binyan, Zhou Jinghao and Chen Shengzhang (Grade 2024 Master’s students of Nursing from our school) as co-first authors, and Professor Cai Fuman and Dr. Huang Pan as co-corresponding authors, the paper achieved 334 downloads within three months and ranked among the top articles published in the journal in Q2 2025. For these outstanding achievements, the research team was awarded the "2025 Q2 Wiley China Highly Cited Author Award", demonstrating the far-reaching international academic influence of this study.
Based on the large-scale critical care database MIMIC-IV, this study conducted a retrospective analysis of data from 1,774 ICU patients with pressure ulcers. It combined LASSO regression and the Boruta algorithm for feature selection and constructed 9 machine learning prediction models. The results showed that the CatBoost model based on the Braden Scale exhibited the best performance in predicting the 90-day mortality risk of ICU pressure ulcer patients (AUC = 0.928), significantly outperforming traditional scoring systems (e.g., SOFA, SAPS II). Furthermore, the study adopted the SHAP method to provide visual interpretation of the model, revealing a significant negative correlation between Braden scores and the mortality rate of ICU patients with pressure ulcers.
In recent years, the Chronic Wound Research Team of our school has focused on research concerning the full-process management of chronic wounds, and established a four-in-one research system integrating basic animal experiments, artificial intelligence analysis of clinical data, multi-center database mining, and clinical application and translation.
To date, the team has published nearly 30 high-quality papers in prestigious domestic and international journals, including British Journal of Dermatology (IF=9.6), Journal of Advanced Research (IF=13.0), Mater Today Bio (IF=10.2) and Chinese Journal of Nursing; several of these papers have been selected as the "Top 100 Excellent Papers" by the Chinese Nursing Association. The team has secured 9 national invention patents and 7 utility model patents, with 2 patents already translated into industrial production. In addition, the team has compiled 3 international clinical practice guidelines on wound care and 1 academic monograph, and built an infrared thermography database of over 40,000 chronic wound cases. Its research outcomes have won the Second-Class Science and Technology Award of the Chinese Nursing Association and been presented at numerous domestic and international academic conferences.
The Wiley China Highly Cited Author Award was launched to respond to the innovation-driven strategy proposed in China’s National 15th Five-Year Plan for Talent Development, deepen cooperation with the Chinese scientific research community, and support the global development of basic research and technological innovation. Wiley statistics the full-text download volume of all articles published by Chinese authors (whose corresponding authors are affiliated with institutions in mainland China) in Wiley journals recognized by Chinese researchers within three months of publication. From each journal, several articles with the highest download volumes written by Chinese authors are selected, and all Chinese authors affiliated with mainland Chinese institutions are awarded the title of "Wiley China Highly Cited Author" in recognition of their significant contributions to scientific and technological progress.
