首页|基于大数据的医疗风险综合评估系统的设计

基于大数据的医疗风险综合评估系统的设计

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目的 构建基于大数据的医疗风险综合评估系统并进行一致性、效率评估。方法 针对住院患者风险评估现状,基于大数据的手段,应用医学自然语言处理设计医疗风险综合评估系统。系统可自动抓取患者的各项数据,通过数据挖掘和机器学习技术自动生成评分,并将风险数据发送给医护人员,从而实现医疗风险评估自动化、智能化。采用随机对照分析,分别对纳入的评分量表进行人工赋分和机器赋分,比较赋分用时,自动生成可视化风险矩阵图。结果 该系统纳入研究的评分系统Kappa值结果如下:Caprini量表(外科)、Padua 量表(内科)Kappa 值为 1。00,NNIS Kappa 值为 1。00,Nomogram Kappa 值为 0。87,Morse 评估量表/Hendrich 模型 Kappa 值为 0。83,Braden Kappa值为 0。80,ASA2023 Kappa 值为 1。00,NRS2002 Kap-pa值为0。90。机器赋分用时均短于人工赋分用时,差异有统计学意义(P<0。05)。结论 通过本系统构建的风险矩阵图可使评估效率和准确性大幅提升,不仅能提供精准的诊疗方案,还能缩短患者住院时间,降低医疗费用。
Design of medical risk comprehensive assessment system based on big data
Objective To construct the medical risk comprehensive assessment system based on big data,and to evaluate its consistency and efficiency.Methods Aiming at the current situation of risk assessment of inpatients,based on the means of big data,the medical natural language processing was used to design a medi-cal risk comprehensive assessment system.The system can automatically capture various data of patients,au-tomatically generate the scores by data mining and machine learning technology and send the risk data to med-ical staff,so as to realize the automation and intellectualization.The randomized controlled analysis was used to conduct the manual scoring and machine scoring for included the score scale.The visual risk matrix diagram was automatically generated by comparing the scoring.Results The Kappa values of the scoring system in the included study of the system were as follows:the Kappa value in Caprini scale(surgery)and Padua scale(internal medicine)was 1.00,NNIS Kappa value was 1.00,Nomogram Kappa value was 0.87,Kappa value in the Morse assessment scale/Hendrich model was 0.83,Braden Kappa value was 0.80,ASA 2023 Kappa was 1.00 and NRS 2002 Kappa value was 0.90.The taking time in the machine scoring all were shorter than those in the manual scoring,and the difference was statistically significant(P<0.05).Conclusion The risk matrix graph constructed by this system could sharply increase the evaluation efficiency and accuracy,which not only provide the accuracy diagnosis and treatment regimen,but also shorten the hospitalization duration and reduce the medical costs.

risk assessmentsystem designbig datanatural language processingevaluation automa-tion

蒋丽梅、刘锋、杜倩、戴黎阳、张杨、严敏

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重庆医科大学附属第三医院(捷尔医院),重庆 401120

风险评估 系统设计 大数据 自然语言处理 评估自动化

2024

重庆医学
重庆市卫生信息中心,重庆市医学会

重庆医学

CSTPCD
影响因子:1.797
ISSN:1671-8348
年,卷(期):2024.53(17)