皮肤增生性疾病激光治疗数据库的构建与应用  

Construction and Application of a Database for Laser Treatment of Skin Proliferative Diseases

作　　者：李婷[1,2] 张景伟 李博尉 陈奕萱 贾玉颖 李晓珍 李海娇 蔡宏 LI Ting;ZHANG Jingwei;LI Bowei;CHEN Yixuan;JIA Yuying;LI Xiaozhen;LI Haijiao;CAI Hong(Hebei North University,Zhangjiakou 075000,China;Department of Dermatology,Air Force Medical Center;Institute of Computing Technology,Chinese Academy of Sciences)

机构地区：[1]河北北方学院,张家口市075000 [2]空军特色医学中心皮肤科 [3]中国科学院计算技术研究所

出　　处：《中国激光医学杂志》2025年第1期12-17,共6页Chinese Journal of Laser Medicine & Surgery

基　　金：军队后勤保障部重点项目(BKJWS221J009)。

摘　　要：目的构建皮肤增生性疾病激光治疗数据库,以规范化收集和处理患者的诊疗信息、激光治疗参数及影像数据,为临床个性化治疗方案的制定和数据驱动的决策提供数据分析。方法回顾性分析2013~2023年,经激光治疗皮肤增生性疾病患者3367例的临床资料,涉及的疾病包括脂溢性角化斑、鲜红斑痣、血管瘤等31种。参考国内外权威医疗标准和相关数据库建设经验,构建皮肤增生性疾病激光治疗数据库,其中模块19个、框架419个字段(涵盖通用字段和专病字段)。通过数据提取-转换-加载技术(extract-transform-load,ETL)对经激光治疗皮肤增生性疾病的患者进行多源异构数据采集。再采用自然语言处理、图像处理技术和Django架构,根据数据的结构化程度对数据进行分级处理,将非结构化数据进行标准化处理。记录构建数据库中数据查询、统计分析和临床决策支持等功能情况。结果皮肤增生性疾病激光治疗数据库的构建累计采集治疗前后对比影像照片22757张,治疗变量101010个。实现了数据查询、统计分析和临床决策支持等功能。基于Django架构和分布式技术,提供了患者诊疗全周期管理,通过多维度检索、数据分析及智能辅助诊断制定出个体化治疗方案制定和预后效果的预测。结论皮肤增生性疾病激光治疗数据库实现了多维度数据查询、数据统计和临床决策支持功能,支持个体化治疗方案的制定和预后效果的预测,显著提升了治疗效果评估的准确性和数据管理的规范化水平。Objective To construct a laser treatment database for proliferative skin diseases with a view to standardizing the collection and processing of patients'medical information,laser treatment parameters and imaging data and providing data analysis to support the formulation of personalized clinical treatment plans and data-driven decision-making.Methods A retrospective analysis was conducted on the clinical data of 3,367 patients with proliferative skin diseases given laser therapy between 2013 and 2023,encompassing 31 conditions including seborrheic keratosis,hemangioma and vascular malformations.By reference to authoritative domestic and international medical standards and relevant database construction experiences,a laser treatment database for proliferative skin diseases was developed,comprising 19 modules and 419 fields(including general and disease-specific fields).With the help of extract-transform-load(ETL)techniques,multi-source heterogeneous data were collected from patients given laser treatment for proliferative skin diseases.Additionally,natural language processing,image processing technologies and the Django framework were employed to classify and process the data based on its degree of structuring and standardize unstructured data.The functions of data querying,statistical analysis and clinical decision support within the constructed database were documented.Results The construction of the laser treatment database for proliferative skin diseases accumulated 22,757 pre-and post-treatment comparative images and 101,010 treatment variables.Functions such as data querying,statistical analysis and clinical decision support were successfully implemented.Based on the Django framework and distributed technologies,comprehensive management of the patient's entire diagnostic and treatment cycle was realized.Through multi-dimensional retrieval,data analysis and intelligent assisted diagnosis,personalized treatment plans and prognosis predictions were developed.Conclusions The laser treatment database for prolifer

关 键 词：皮肤增生性疾病 激光治疗 数据库 数据标准化 临床研究 

分 类 号：R730.57[医药卫生—肿瘤] R751.05[医药卫生—临床医学]