检索结果-内蒙古大学图书馆

Application of medical intelligence based on apriori algorithm in the management of rehabilitation nursing personnel

引用

SOFT COMPUTING 2023年 1-10页

作者： Shi, Minmin Wang, Qiuyue Guo, Feng Zhejiang Chinese Med Univ Sch Nursing Hangzhou 310000 Peoples R China Xiangyang Polytech Xiangyang 441050 Hubei Peoples R China Wenzhou Business Coll Sch Management Wenzhou 325000 Peoples R China

With the increasing demand for medical services, the amount of data in the field of health care is gradually increasing in a stepwise manner every year. These medical data actually capture the medical information of patients and the entire diagnosis and treatment process of doctors and can reflect the actual situation in the medical and health field. Currently, most hospital rehabilitation nurses use simple statistical methods to process medical data, and the use value of medical data resources is limited. To improve the value of using medical information, check the relationship between hidden laws and medical information. As one of the effective methods in the field of data mining, apriori's basic principle is to use specific association rules to define all common element sets that support exceeding the minimum support threshold, and to create association rules that use a common model to achieve the validity threshold. This article uses apriori algorithm to detect rehabilitation nursing personnel in hospitals, conduct data mining and exploration on medical data, design and construct a medical intelligence system, and then use apriori algorithm to conduct research, propose a disease classification model and inpatient flow model, and conduct training and testing through the disease classification model. Finally, 300 hospital rehabilitation nursing personnel have received training in using a medical intelligence system to test the actual performance of the system, which has proven to enable rehabilitation nursing personnel to better understand patient information and provide personalized services.

关键词： apriori algorithm Medical intelligence Rehabilitation nursing personnel Association rules

来源：评论

学校读者我要写书评

暂无评论

Identification of polypharmacy patterns in new-users of metformin using the apriori algorithm: A novel framework for investigating concomitant drug utilization through association rule mining

引用

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2023年第3期32卷 366-381页

作者： Faquetti, Maria Luisa la Torre, Adrian Martinez-De Burkard, Theresa Obozinski, Guillaume Burden, Andrea M. Swiss Fed Inst Technol Inst Pharmaceut Sci Dept Chem & Appl Biosci Zurich Switzerland Ecole Polytech Fed Lausanne Swiss Data Sci Ctr SDSC Lausanne Switzerland Swiss Fed Inst Technol Inst Pharmaceut Sci Dept Chem & Appl Biosci HCI H 407 Vladimir Prelog Weg 4 CH-8093 Zurich Switzerland

Purpose: With increased concomitant chronic diseases in type 2 diabetes mellitus (T2DM), the use of multiple drugs increases as well as the risk of drug-drug interac-tions (DDI) and adverse drug reactions (ADR). Nevertheless, how medication patterns vary in T2DM patients across different sex and age groups is unclear. This study aims to identify and quantify common drug combinations in first-time metformin users with polypharmacy (>_5 co-medications).Methods: New users of metformin were identified from the IQVIA Medical Research Data incorporating data from THIN, A Cegedim Database (2016-2019). A descriptive cohort study explored prescription patterns in patients with polypharmacy. The apriori algorithm, used to find frequent item-sets in databases, was first-time applied to identify and quantify drug combinations of up to seven drugs to investigate poten-tial harmful polypharmacy ***: The cohort included 34 169 new-users of metformin, of which 20 854 (61.0%) received polypharmacy. Atorvastatin was the most frequently co-prescribed drug with metformin overall (38.7%), in women (34.3%) and men (42.6%). In the strati-fied analysis, a higher proportion of women received polypharmacy (65.6%) compared to men (57.4%). Moreover, the proportion of patients receiving polypharmacy increased with age (18-39 years = 30.4%, 40-59 years = 50.5%, 60-74 years = 70.9%, and >= 75 years = 84.3%).Conclusion: This study is the first to identify and quantify commonly prescribed com-binations of drugs compounds in patients with polypharmacy using the apriori algorithm. The high polypharmacy prevalence at all strata indicates the need to optimize polypharmacy to minimize DDI and ADR.

关键词： apriori algorithm diabetes mellitus type 2 drug interactions drug utilization polypharmacy potentially inappropriate medications prescription patterns

来源：评论

学校读者我要写书评

暂无评论

Developing a novel H-apriori algorithm using support-leverage matrix for association rule mining

引用

International Journal of Information Technology (Singapore) 2024年第8期16卷 5395-5405页

作者： Pamnani, Harish Kumar Raja, Linesh Ives, Thom Department of Computer Applications Manipal University Jaipur Rajasthan Jaipur India Echo Global Logistic 600 W Chicago Ave #725 Chicago 60654 IL United States

The importance of e-commerce platforms is playing a vital role in human life. Data mining plays a crucial role in e-commerce platforms, allowing them to extract valuable information from extensive datasets by examining buying tendencies from customers using Association rule mining and Market basket analysis. In this research paper, the author proposed a heuristic H-apriori algorithm built on the support-leverage of an item and the effective weight of a transaction using a single dataset scan. The trial estimation contains five sample transactional datasets with variable sizes, ranging from 20 to 7500 transactions. The performance of H-apriori algorithm is compared with state-of-the-art algorithms, Improved-A priori, Hybrid-apriori, and classic Fp-Growth. Improved-apriori and Hybrid-apriori are relying on queue data structure, while classic Fp-Growth is based on a tree data structure. The results of this study demonstrate that the proposed heuristic H-apriori algorithm outperforms the improved A priori, Hybrid-apriori, and Fp-Growth with accuracy of frequent item set and interesting item set count, taking up to 99%,75%, and 93% less run time for frequent item set and up to 91% for interesting item set, as well as up to 91%,15%, and 13% less memory consumption. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： apriori algorithm Association Rule Mining Data Mining Frequent item mining

来源：评论

学校读者我要写书评

暂无评论

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and apriori algorithm

引用

Chinese Journal of Mechanical Engineering 2022年第5期35卷 66-86页

作者： Cunbo Zhuang Ziwen Liu Jianhua Liu Hailong Ma Sikuan Zhai Ying Wu Laboratory of Digital Manufacturing School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing 314000China Shanghai Institute of Spacecraft Equipment Shanghai 200240China

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent *** to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering *** the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such ***,a quality management method for the assembly process of aerospace products based on DT is *** that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly *** apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to *** implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

关键词： Quality management Digital twin Assembly process Aerospace product Grey Markov model apriori algorithm

来源：评论

学校读者我要写书评

暂无评论

Performance of a Distributed apriori algorithm Using the Serverless Functions of the Apollo Framework 13th

Performance of a Distributed Apriori Algorithm Using the Ser...

引用

13th Computer Science Online Conference

作者： Shahin, Mahtab Shah, Syed Attique Sharma, Rahul Ghasempouri, Tara Poveda, Juan Aznar Fahringer, Thomas Draheim, Dirk Tallinn Univ Technol Informat Syst Grp EE-12616 Tallinn Estonia Birmingham City Univ Sch Comp & Digital Technol Birmingham B4 7XG W Midlands England Tallinn Univ Technol Dept Comp Syst EE-12616 Tallinn Estonia Univ Innsbruck Distributed & Parallel Syst Grp A-6020 Innsbruck Austria

ISBN: (纸本)9783031705946;9783031705953

Early diagnosis and accurate judgment are paramount in cancer diagnosis and treatment. Establishing an effective cancer early warning system is crucial to improve patient outcomes. Data mining technology, particularly association rule mining, plays a vital role in cancer surveillance and early warning by processing large datasets. Our study focuses on lung cancer, one of the leading causes of death worldwide. Despite numerous approaches, challenges such as high computational costs and memory limitations persist when attempting to extract meaningful rules from databases. In this paper, we propose leveraging the apriori algorithm within the Apollo framework, based on the Apollo multi-cloud orchestration framework developed by the University of Innsbruck, for distributed association rule mining. By harnessing serverless functions, we achieve distributed processing, enhancing scalability and performance. Our experiments demonstrate that Apollo outperforms Apache Spark in terms of speed (about 15%), and extracts more rules. The results highlight the efficacy of distributed association rule mining using serverless functions for cancer early warning systems. We conclude that this approach shows promise and warrants further exploration and extension in future research endeavors.

关键词： distribution apriori algorithm association rule mining parallel framework machine learning

来源：评论

学校读者我要写书评

暂无评论

Association Analysis of Automotive Faulty Equipment Based on apriori algorithm 24

Association Analysis of Automotive Faulty Equipment Based on...

引用

International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Chen, Xiuxiu Jia, Lianqin Yan, Qing Shandong Inst Commerce & Technol Intelligent Robot Ind Coll Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9798400709784

This paper proposes an association analysis method for automotive faulty equipment based on apriori algorithm. It is applied to association rules mining of automotive faulty equipment, and the fault data derived from vehicle detection system are extracted and the association rules mining pattern is established so that the strong association rules among the faulty equipments are mined for fault analysis. The experimental results prove that the apriori algorithm can effectively mine the association relationships among automotive faulty equipments.

关键词： apriori algorithm Association rules Fault analysis

来源：评论

学校读者我要写书评

暂无评论

Design of Intelligent Elderly Care Positioning Management System Based on apriori algorithm 24

Design of Intelligent Elderly Care Positioning Management Sy...

引用

International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Li, Wei Shandong Inst Commerce & Technol Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9798400709784

The advent of the era of big data brings convenience to the positioning of smart elderly care, but at the same time, the data generated is also countless, and people can not obtain effective information in a timely manner. Information overload, the internal relationship between different factors and different transactions can not be presented. The emergence of data mining technology helps people to explore the special relationship within the transaction, so as to further predict and make decisions. This paper mainly focuses on association rules, chooses apriori algorithm, introduces the concept of the algorithm, and analyzes the advantages and disadvantages of the algorithm in use. The classic apriori algorithm can effectively discover the hidden internal relationship between data, but it also has the problem that the larger the number of candidate sets, the higher the cost. To solve this problem, a new improved algorithm RTI apriori is proposed in this paper. The algorithm improves on the shortcomings of traditional apriori algorithm. Its idea is to introduce Boolean matrix to store transaction information. The rows of matrix correspond to the transaction set in the transaction database, and the columns of matrix correspond to the items in the transaction set. By scanning the Boolean matrix constructed by transaction database and the "logical and" operation of the matrix, the corresponding association rules are found. At the end of the matrix, 1 row and 2 columns are added to help calculate the support of the item set, compress the matrix, so as to simplify the operation, and then delete the item set that does not meet the conditions according to the result of scanning. At the same time, parallel method is introduced to optimize the algorithm in parallel, so as to improve the efficiency of the algorithm. Experiments show that the improved algorithm does not need to scan the database many times, and is more efficient than the original algorithm.

关键词： apriori algorithm Intelligent elderly care Association rules RTI_apriori algorithm Logical operation

来源：评论

学校读者我要写书评

暂无评论

Research on Pricing and Replenishment Decision Making Based on ARIMA Time Series and apriori algorithm 2

Research on Pricing and Replenishment Decision Making Based ...

引用

2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Wu, Na Rao, Xinrui Li, Donglin China University of Geosciences Beijing Beijing China

ISBN: (纸本)9798350386639

This study addresses the sales and pricing issues of vegetable items in fresh produce superstores. Evaluation and analysis are conducted for six categories of vegetable merchandise to reveal the correlation between the popular single products in each category and the vegetable category through the gray correlation method and Pearson correlation analysis method. In terms of formulating the total daily replenishment and pricing strategy for the coming week that maximizes the profit of the superstore, an ARIMA time-series model is established, combined with the apriori algorithm and data visualization methods, to formulate a pricing strategy that meets the actual situation. For the replenishment plan of individual products, through the analysis of the sales data of the past three years, the top thirty-three selling individual products are selected as the sellable varieties, and the seasonal decomposition is used to predict the data of each individual product and formulate the replenishment and pricing strategies. Finally, data factors such as temperature impact and consumer demand are combined to provide reference for superstores to plan better replenishment and pricing strategies. Based on actual data and mathematical modeling, this study provides feasible suggestions for superstores to improve revenue and reduce wastage rate, which promotes superstores' operational efficiency and market competitiveness. © 2024 IEEE.

关键词： apriori algorithm ARIMA time series Gray correlation analysis

来源：评论

学校读者我要写书评

暂无评论

Protection of data privacy from vulnerability using two-fish technique with apriori algorithm in data mining

引用

JOURNAL OF SUPERCOMPUTING 2022年第16期78卷 17559-17593页

作者： Dhinakaran, D. Prathap, P. M. Joe Anna Univ Informat & Commun Engn Chennai Tamil Nadu India RMD Engn Coll Dept Informat Technol Kavaraipettai Tiruvallur India

The confidential data is mainly managed by creating passwords, tokens, and unique identifiers in an authorized manner. These records must be kept in a safe location away from the reach of unauthorized third parties. Both the client and server sides must be encrypted using the two-fish algorithm, which secures the distinction of private data. By gaining access to the user's information, a data miner may be able to steal it. To avoid such situations, both the data miner and the server must be encrypted. Further, the previous techniques faced several shortcomings in case of higher computational overhead, poor resource utilization, prone to single point failure, lower accuracy, noise, poor security, higher distortion, etc. In this study, both the client and server sides are encrypted using a two-fish algorithm to avoid information loss while transferring data to overcome these problems. The way the state-of-art techniques handled the privacy preservation issue often leads to privacy violations. This paper focuses on mining frequent itemsets present in the medical data by also ensuring privacy. Frequent itemset mining mainly aims to extract highly correlated items from the database and to achieve this novel fruitfly whale optimization algorithm (FWOA) combined with the apriori algorithm. The apriori heuristic and bio-inspired algorithms are integrated to solve the frequent itemset problem by reducing the low runtime performance when handling large datasets and also offering high-quality solutions. The adaptive k-anonymity approach is used for preserving data privacy by transforming the original data into an encrypted mode and offering privacy to the top-k frequent itemsets mining. The main advantage of the adaptive k-anonymity approach is that the confidential information disclosed by an individual user cannot be identified from at least k - 1 individuals. We ensure that the proposed methodology can offer data privacy in real time by the experiments conducted in a medica

关键词： apriori algorithm Data miner Two-fish algorithm Privacy preservation Bio-inspired algorithms Association rule mining Adaptive k-anonymity approach

来源：评论

学校读者我要写书评

暂无评论

Correlation Analysis of Sensor Fault Based on Fuzzy Petri Net and apriori algorithm 6th

Correlation Analysis of Sensor Fault Based on Fuzzy Petri Ne...

引用

Conference of The-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th International conference on Maintenance Engineering (IncoME)

作者： Xu, Chuannuo Zhao, Shenglei Hao, Haitao Zhang, Yandong Li, Jiming Cheng, Xuezhen Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Anju Coal Mine Shandong Fining Min Grp Co Ltd Jining 272000 Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Due to the complex internal structure of the sensor, the corresponding fault causes are also diverse. Once a fault occurs, the cause of the fault is difficult to determine. This paper proposes a sensor fault correlation analysis method combining fuzzy Petri net (FPN) and apriori algorithm. First, obtain the typical fault type waveform of the sensor according to the method of fault simulation, calculate its fault waveform characteristics, find out the residual between it and the normal waveform characteristics, and normalize the residual;then, use the modeling method of FPN to establish the correlation analysis model between fault types, fault characteristic indicators and fault modes;finally, the establishment of model weights and transition threshold parameters is achieved through the apriori algorithm based on association rules. The maintainer can analyze the fault correlation of the sensor through the abnormal waveform of the sensor to preliminarily judge the fault cause, to achieve the purpose of improving the efficiency of maintenance.

关键词： Sensor Fault causes Fuzzy petri net apriori algorithm Correlation analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：