Purpose: Radiation-induced dermatitis (RD) is a common side-effect of therapeutic ionizing radiation that can severely affect patient quality of life. This study aimed to develop a risk prediction model for the occurrence of RD in patients with cervical carcinoma undergoing chemoradiotherapy using electronic medical records (EMRs). Methods: Using EMRs, the clinical data of patients who underwent simultaneous radiotherapy and chemotherapy at a tertiary cancer hospital between 2017 and 2022 were retrospectively collected, and the patients were divided into two groups: a training group and a validation group. A predictive model was constructed to predict the development of RD in patients who underwent concurrent radiotherapy and chemotherapy for cervical cancer. Finally, the model's efficacy was validated using a receiver operating characteristic curve. Results: The incidence of radiation dermatitis was 89.5% (560/626) in the entire cohort, 88.6% (388/438) in the training group, and 91.5% (172/188) in the experimental group. The nomogram was established based on the following factors: age, the days between the beginning and conclusion of radiotherapy, the serum albumin after chemoradiotherapy, the use of single or multiple drugs for concurrent chemotherapy, and the total dose of afterloading radiotherapy. Internal and external verification indicated that the model had good discriminatory ability. Overall, the model achieved an area under the receiver operating characteristic curve of .66. Conclusions The risk of RD in patients with cervical carcinoma undergoing chemoradiotherapy is high. A risk prediction model can be developed for RD in cervical carcinoma patients undergoing chemoradiotherapy, based on over 5 years of EMR data from a tertiary cancer hospital.

Purpose/Significance Through the establishment of a quality control system for electronic medical record(EMR)con-tent,the standardization and normalization of medical record writing is realized,and the quality of hospital medical record is improved.Method/Process The intelligent medical data center is built based on hospital medical data,and the knowledge base and rule base with tumor specialty characteristics are formed by combining natural language processing(NLP)and machine learning technology.The new quality control mode of"pre-audit,comprehensive coverage,process supervision and closed-loop management"of EMR is realized.Result/Conclusion After the application of the medical record quality control system based on NLP,the quality control coverage rate in-creased from 1%to 100%,and the rate of class A medical records increased to more than 96%,with good real-time and accuracy,providing a solid information foundation for the high-quality development of hospital medical records.