The intelligent experience inheritance system for Traditional Chinese Medicine

遗传(遗传算法) 计算机科学 生物 遗传学 基因
作者
Xue Ren,Yan Guo,Heyuan Wang,Xiang Gao,Wei Chen,Xiaogang Wang
出处
期刊:Journal of Evidence-based Medicine [Wiley]
卷期号:16 (1): 91-100 被引量:4
标识
DOI:10.1111/jebm.12517
摘要

The inheritance of knowledge and experience was crucial to the development of Traditional Chinese Medicine (TCM). However, the existing methods of inheriting the unique clinical experience of famous veteran TCM doctors still followed the outdated and inefficient Master-Prentice schema. In addition, the inherited medical books and records were usually lack of standardization and systematization. In this article, a new method for inheriting the academic thoughts and clinical experience of famous veteran doctors with the help of artificial intelligence technology was explored. Due to the individualized treatment characteristics namely "same disease with different treatments, different diseases with the same treatment," the intelligent inheritance of TCM faced many technical barriers. To tackle these problems, we proposed a prototype system framework for the intelligent inheritance of famous veteran doctors based on rules and deep learning models and performed a case study on the treatment of pediatric asthma. The architecture could not only make full use of the advantages of deep learning, but also integrate the valuable knowledge and experience analysis of famous veteran doctors from injected rules. Specifically, the study took pediatric asthma medical records as training and test samples and calculated the similarity between the generated prescriptions and the real-world clinical prescriptions from the famous veteran doctors. Experimental results showed that the generated prescription could achieve a similarity of more than 90%. It proved that the proposed framework provided a feasible way for the intelligent inheritance and research of the academic thoughts and clinical experience of famous veteran TCM doctors.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
大个应助茹茹采纳,获得10
1秒前
1秒前
聆听完成签到,获得积分10
2秒前
起点完成签到,获得积分10
4秒前
5秒前
踏实半仙完成签到,获得积分10
8秒前
9秒前
至秦发布了新的文献求助30
10秒前
10秒前
11秒前
冷咖啡离开了杯垫完成签到,获得积分10
12秒前
AT完成签到,获得积分10
15秒前
Arosy发布了新的文献求助10
15秒前
zorro3574完成签到,获得积分10
17秒前
20秒前
英姑应助纳兰若微采纳,获得10
20秒前
领导范儿应助小黎快看采纳,获得10
21秒前
21秒前
申申发布了新的文献求助10
23秒前
雨后完成签到,获得积分10
23秒前
24秒前
26秒前
hi发布了新的文献求助10
26秒前
27秒前
天天开心完成签到 ,获得积分10
27秒前
淡定山柏发布了新的文献求助10
28秒前
背后访风完成签到 ,获得积分10
29秒前
陶醉大侠发布了新的文献求助10
30秒前
buno应助别看我只是一只羊采纳,获得10
31秒前
Amor发布了新的文献求助10
31秒前
张张完成签到,获得积分20
32秒前
orixero应助hurry采纳,获得10
34秒前
34秒前
酷炫怀莲完成签到,获得积分10
36秒前
37秒前
ms发布了新的文献求助10
38秒前
不会起名发布了新的文献求助10
39秒前
40秒前
研友_VZG7GZ应助Amor采纳,获得10
40秒前
40秒前
高分求助中
Lire en communiste 1000
Ore genesis in the Zambian Copperbelt with particular reference to the northern sector of the Chambishi basin 800
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 700
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 700
Becoming: An Introduction to Jung's Concept of Individuation 600
Evolution 3rd edition 500
Die Gottesanbeterin: Mantis religiosa: 656 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3171135
求助须知:如何正确求助?哪些是违规求助? 2822063
关于积分的说明 7937837
捐赠科研通 2482500
什么是DOI,文献DOI怎么找? 1322565
科研通“疑难数据库(出版商)”最低求助积分说明 633669
版权声明 602627