Design of medical risk comprehensive assessment system based on big data
10.3969/j.issn.1671-8348.2024.17.021
- VernacularTitle:基于大数据的医疗风险综合评估系统的设计
- Author:
Limei JIANG
1
;
Feng LIU
;
Qian DU
;
Liyang DAI
;
Yang ZHANG
;
Min YAN
Author Information
1. 重庆医科大学附属第三医院(捷尔医院),重庆 401120
- Keywords:
risk assessment;
system design;
big data;
natural language processing;
evaluation automa-tion
- From:
Chongqing Medicine
2024;53(17):2672-2676
- CountryChina
- Language:Chinese
-
Abstract:
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.
