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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (12): 98-109    DOI: 10.11925/infotech.2096-3467.2021.0583
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Predicting Related Medical Topics from Social Media
Wu Shengnan1,Tian Ruonan2,Pu Hongjun1,Liang Wenqi2,Zhang Yafei2,Yu Qi1,He Peifeng1,2()
1School of Management, Shanxi Medical University, Taiyuan 030000, China
2School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan 030000, China
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Abstract  

[Objective] This paper proposes a new knowledge discovery method for social media, aiming to predict the topic-related opportunities and emerging topics in medicine.[Methods] We developed a method combining the Co-LDA topic model and the link prediction algorithm to identify topic association opportunities. We examined the new model with data on diabetes drugs from social media. [Results] The AUC value of the prediction for the common network link without the right topics was higher than those with the right topics, while the Katz index is the optimal one. The future research on diabetes drugs is most likely to be related to the improvement of pharmacodynamic research and treatment plans. The development of the pharmaceutical industry and the new drug indications were related. [Limitations] We did not conduct multi-level analysis with emotional and time dimensions, and the new algorithm is very complex and did not perform well with poor network connectivity. [Conclusions] The proposed method could effectively predict the topic association opportunities in the field of medicine.

Key wordsKnowledge Discovery      Topic Association      LDA      Link Prediction     
Received: 15 June 2021      Published: 20 January 2022
ZTFLH:  G250  
Fund:National Natural Science Foundation of China(71804102);National Natural Science Foundation of China(71573162);Philosophy and Social Science Research Project of Colleges and Universities in Shanxi Province(71573162)
Corresponding Authors: He Peifeng,ORCID:0000-0002-3742-6983     E-mail: hepeifeng2006@126.com

Cite this article:

Wu Shengnan, Tian Ruonan, Pu Hongjun, Liang Wenqi, Zhang Yafei, Yu Qi, He Peifeng. Predicting Related Medical Topics from Social Media. Data Analysis and Knowledge Discovery, 2021, 5(12): 98-109.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0583     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I12/98

Subject Recognition Method Based on Co-LDA
Link Prediction Based on Thematic Co-Occurrence Network
指标 基于节点局部信息的相似性指标
不含权算法 含权算法
AA S xy = z ( x ) ? Γ ( y ) 1 lg k ( z )[20] S y x = z Γ ( x ) ? Γ ( y ) W xz + W zy 2 lg ( 1 + S z )[21]
PA S xy = k ( x ) k ( y )[22] S xy PA = x ' ( x ' , x ) w ( x ' , x ) y ' ( y ' , y ) w ( y ' , y )[23]
RA S xy = z Γ ( x ) ? Γ ( y ) 1 k ( z )[24] S xy RA = z Γ ( x ) ? Γ ( y ) W xz + W zy S z[21]
CN S xy = | Γ ( x ) ? Γ ( y ) |[25] S xy CN = z Γ ( x ) ? Γ ( y ) W xz + W zy[21]
Salton S xy = | Γ ( x ) ? Γ ( y ) | k ( x ) k ( y ) [26] S xy salton = z Γ ( x ) ? Γ ( y ) W xz + W zy s ( x ) s ( y ) [27]
Jaccard S xy = | Γ ( x ) ? Γ ( y ) | | Γ ( x ) ? Γ ( y ) |[28] S xy Ja = z Γ ( x ) ? Γ ( y ) W xz + W zy s ( x ) + s ( y ) - w ( x , y )[27]
Sorenson S xy = 2 | Γ ( x ) ? Γ ( y ) | k ( x ) + k ( y )[29] S xy so = 2 z Γ ( x ) ? Γ ( y ) W xz + W zy s ( x ) + s ( y ) 1 2[27]
大度节点有利 S xy = | Γ ( x ) ? Γ ( y ) | min k ( x ) , k ( y )[30] S xy = z Γ ( x ) ? Γ ( y ) W xz + W zy min s ( x ) , s ( y )[27]
大度节点不利 S xy = | Γ ( x ) ? Γ ( y ) | max k ( x ) , k ( y ) [31] S xy = z Γ ( x ) ? Γ ( y ) W xz + W zy max s ( x ) , s ( y )[27]
LHN-Ⅰ S xy = | Γ ( x ) ? Γ ( y ) | k ( x ) k ( y )[31] S xy LHN = z Γ ( x ) ? Γ ( y ) W xz + W zy s ( x ) s ( y )[27]
指标 基于路径的相似性指标
LP S = A 2 + A 3[26] S xy LP = l xy n L ( x , y ) l xy 2 l xy 3[28]
Katz S = ( I - A - 1 ) - I [32]
小于A最大特征值的倒数
S = ( I - A ) - 1 - I[32]
A为含权矩阵,且 小于A最大特征值的倒数
Definition of Link Prediction Similarity Index
药物类别 代表药物
双胍类 二甲双胍(格华止、美迪康)
苯乙双胍(降糖灵)
磺脲类 格列本脲(优降糖)、格列美脲、格列齐特(达美康)
格列吡嗪(美吡达)、格列喹酮(糖适平)
TZDs 罗格列酮、吡格列酮、文迪雅
格列奈类 瑞格列奈、诺和龙、那格列奈、米格列
α-糖苷酶抑制剂 阿卡波糖(拜糖平)、伏格列波糖
DPP-4抑制剂 西格列汀、沙格列汀、利格列汀、阿格列汀、捷诺维
SGLT2抑制剂 达格列净、恩格列净、卡格列净
复方制剂 消渴丸
Type 2 Diabetes Drugs
JS Distance Curve of Diabetes Drug Theme Model
主题编号 主题名 主题编号 主题名
Topic1 国家集中药品采购 Topic22 新闻媒体对降糖药的报道
Topic2 降低药品价格,改善行业生态 Topic23 2型糖尿病治疗方案研究
Topic3 国内糖尿病高仿药上市 Topic24 临床试药员招募
Topic4 二甲双胍和胰岛素联合用药疗效显著 Topic25 药物作用机制
Topic5 保健品非法添加苯乙双胍,病死率增加 Topic26 药物疗效与饮食控制的关联研究
Topic6 药物剂量与不良反应的关系 Topic27 用药后疗效追踪
Topic7 二甲双胍和胰岛素联合治疗产生不良反应 Topic28 胰岛素介入治疗效果研究
Topic8 两病联合治疗效果 Topic29 调节血糖类保健产品功效介绍
Topic9 二甲双胍可降低糖耐量受损人群患病风险 Topic30 二甲双胍药物不良反应研究
Topic10 格列吡嗪通过促进胰岛素的分泌降低血糖 Topic31 阿卡波糖和二甲双胍联合治疗研究
Topic11 二甲双胍疗效研究 Topic32 药物服用方法
Topic12 消渴丸(成分含格列本脲)治疗风险 Topic33 二甲双胍与癌细胞关联研究
Topic13 DPP4抑制剂降糖效果 Topic34 磺脲类药物致胃肠道反应及肝功能损害副作用
Topic14 降糖药疗效比对研究 Topic35 阿卡波糖与药物间相互作用研究
Topic15 生活方式干预治疗效果 Topic36 卒中保护效应研究
Topic16 二甲双胍与多囊卵巢综合征间关系研究 Topic37 格列本脲适宜用药人群研究
Topic17 二甲双胍无效时,可引入磺脲类药物 Topic38 二甲双胍格列齐特片疗效研究
Topic18 阿卡波糖临床治疗效果 Topic39 饮食疗法
Topic19 SGLT-2抑制剂类有肾脏保护作用 Topic40 糖尿病前期人群预防方法
Topic20 胰岛素治疗致不良心血管事件发生率升高 Topic41 二甲双胍新适应症
Topic21 西格列汀安全性研究 Topic42 低血糖与降糖药关系研究
Diabetes Medications Theme
Diabetes Drug Document-Topic Probability Cumulative Graph
共现主题 共现强度
Topic1+Topic20 10.261
Topic19+Topic29 1.975
Topic3+Topic20 28.213
Topic33+Topic24 0.513
Topic5+Topic12 8.190
Co-Occurrence Intensity of Diabetes Drug Keywords (Partial)
Co-Occurrence Network of Non-Weight in Diabetes Drugs
Co-Occurrence Network of Weight in Diabetes Drugs
算法 AA CN HDI HPI Jaccard LHN-1 PA RA Salton Sorenson LP Katz
不含权 0.964 0.963 0.939 0.165 0.939 0.035 0.958 0.964 0.931 0.939 0.966 0.978
含权 0.942 0.944 0.970 0.095 0.953 0.037 0.955 0.947 0.128 0.953 0.882 0.963
The Average Value of AUC of Each Index with and Without Weight Algorithm
关联主题组合 Katz指标值 相似值
胰岛素治疗致不良心血管事件发生率升高 药物作用机制 1.369 1
保健品非法添加苯乙双胍,病死率增加 2型糖尿病治疗方案研究 0.715 0.484
药物剂量与不良反应的关系 2型糖尿病治疗方案研究 0.705 0.476
国内糖尿病高仿药上市 2型糖尿病治疗方案研究 0.699 0.472
降低药品价格,改善行业生态 2型糖尿病治疗方案研究 0.692 0.466
两病联合治疗效果 2型糖尿病治疗方案研究 0.691 0.466
生活方式干预治疗效果 2型糖尿病治疗方案研究 0.682 0.459
SGLT-2抑制剂类有肾脏保护作用 2型糖尿病治疗方案研究 0.672 0.451
2型糖尿病治疗方案研究 药物作用机制 0.668 0.448
临床试药员招募 二甲双胍格列齐特片疗效研究 0.563 0.364
二甲双胍与癌细胞关联研究 二甲双胍格列齐特片疗效研究 0.554 0.358
Thematic Associations of Diabetes Drugs and Katz Indicators and Similar Values
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