An optimisation approach for determining the efficiency of vital medical devices in intensive care units with COVID-19 patients using Apriori algorithm

Improving the process of strategic management in hospitals preparation and equipping the intensive care units (ICUs) and the availability of medical devices plays an important role for knowing consumer behaviour and need. This cross-sectional study was performed in the ICU of Farhikhtegan Hospital, Tehran, Iran for a period of six months. During these months, ten medical devices have been used 5,497 times. These devices include: ventilator, oxygen cylinder, infusion pump, electrocardiography machine, vital signs monitor, oxygen flowmeter, wavy mattress, ultrasound sonography machine, ultrasound echocardiography machine, and dialysis machine. The Apriori algorithm showed that four devices: ventilator, oxygen cylinder, vital signs monitoring device, oxygen flowmeter are the most used ones by patients. These devices are positively correlated with each other and their confidence is over 80% and their support is 73%. For validating the results, we have used equivalence class clustering and bottom-up lattice traversal (ECLAT) algorithm in our dataset.

Discovering COVID-19 Death Patterns from Deceased Patients: A Case Study in Saudi Arabia

COVID-19 is a serious infection that cause severe injuries and deaths worldwide. The COVID-19 virus can infect people of all ages, especially the elderly. Furthermore, elderly who have co-morbid conditions (e.g., chronic conditions) are at an increased risk of death. At the present time, no approach exists that can facilitate the characterization of patterns of COVID-19 death. In this study, an approach to identify patterns of COVID-19 death efficiently and systematically is applied by adapting the Apriori algorithm. Validation and evaluation of the proposed approach are based on a robust and reliable dataset collected from Health Affairs in the Makkah region of Saudi Arabia. The study results show that there are strong associations between hypertension, diabetes, cardiovascular disease, and kidney disease and death among COVID-19 deceased patients © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Construction of Tibetan Medicine Prescription Database for Treating "Gnyan-rims" and Analysis of Their Medication Law Based on Python Language.

Objective: To construct the database of Tibetan medicine prescriptions for "Gnyan-rims" disease,and to explore the invisible medication law of Tibetan medicine in the treatment of "Gnyan-rims" disease,such as prescription compatibility and combination of drug properties. Method: The prescriptions for treating "Gnyan-rims" were retrieved from four Tibetan medical literatures such as The Four Medical Tantras,Kong-sprul-zin-tig, Phyag-rdor-gso-rig-phyogs-bsgrigs and Sman-sbyor-lag-len-phyogs-bsgrigs, and the database was constructed under Python code,and the Apriori algorithm and the vector structure model of taste property flavor transformation were used for analysis. Result:According to the characteristics of Tibetan medicine prescription data,with six fields of prescription name,formula,dosage,efficacy,source and original text as the core,a Tibetan medicine treatment "Gnyan-rims" prescription database with functions of cleaning, searching and exporting was established. A total of 7 602 prescriptions were included in the database,among which 598 prescriptions had therapeutic effects of "Gnyan" and "Rims". The results of compatibility analysis showed that Shexiang,Hezi,Honghua,Mukuer Moyao,Tiebangchui,Tianzhuhuang and Bangga were the most frequently used drugs,while the correlation degrees of Shexiang-Mukuer Moyao,Honghua-Tianzhuhuang,Shexiang-Hezi and Shexiang-Tiebangchui were the strongest,and all the drug composition of Wuwei Shexiang pills appeared in the top ten correlations. According to the property analysis of 40 prescriptions containing high-frequency drugs,19 prescriptions were found to have excessive bitter taste,followed by 9 prescriptions such as Sanchen powders with excessive sweetish taste,and the ratios of sweetish and bitter tastes in six tastes were >35%. The total of sweetish and bitter prescriptions accounted for 70% of the total prescriptions. Among the three flavors,the bitter flavor was the most abundant. The cool effect,dull effect and heavy effect were prominent among the seventeen effects. Conclusion: The prescription database of Tibetan medicine for "Gnyan-rims" can promote the high-quality development of research on prevention and treatment of plague with ethnic medicine. Tibetan medicine treatment of "Gnyan-rims" focuses on the composition of Wuwei Shexiang pills,with the property combination of "cool-bitter and sweet-bitter flavor-cool,dull and heavy", which mainly treats diseases such as "heat sharp light-mkhris pa-heat". These studies can provide data basis and theoretical reference for the selection of Tibetan medicine prescription and its composition for treating plague.Copyright © 2021, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19

The behaviour of consumers mostly follows the guidelines derived from marketing theories and models. But under some unavoidable circumstances, the consumers show a complete deviation compared to their existing consumption pattern, purchase behaviour, decision-making and so on. Under similar circumstances, this study aims to capture both urban and rural Bottom of the Pyramid (BoP) consumers' perceptions of various marketing mixes during the COVID-19 pandemic situation. With a sample size of 378 and 282, the perception towards different marketing mixes has been captured for Pre-COVID and During-COVID periods, respectively. The adopted quantitative analysis indicates a difference in perception towards marketing mix During COVID compared to Pre-COVID. Moreover, the selection of West Bengal, India, as an area of research fulfills the BoP literature's existing prominent research gap. This study also comes with the potential to assist marketers and the Fast-Moving Consumer Goods (FMCG) industry in framing strategies to target BoP consumers.

Diagnosis of COVID-19 Infection via Association Rules of Cough Encoding

COVID-19 has roused the scientific community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's efficacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and infiuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish coronavirus-induced from non-coronavirus-induced coughs. From this perspective, this research proposes a novel approach for diagnosing COVID-19 infection based on cough sound. The main contributions of this study are the encoding of cough behavior, the investigation of its unique characteristics, and the representation of these traits as association rules. These rules are generated and distinguished with the help of data mining and machine learning techniques. Experiments on the Virufy COVID-19 open cough dataset reveal that cough encoding can provide the desired accuracy (100%). © 2023, ECTI Association. All rights reserved.

Machine Learning Approach: Consumer Buying Behavior Analysis

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

Effects of Coronavirus Disease on Trade for New Zealand

The coronavirus disease 2019 (COVID-19) is a humanitarian crisis that is spreading throughout the world. COVID-19 will be worse to countries that have weak healthcare and economic systems. Countries that are highly affected by coronavirus disease will have problems with international trade since the virus has a high infection rate. This will have effects on the trading economy which will cause export restrictions and trade barriers which make the country trade worse and can cause livelihood problems for the country. But there are countries that handle the pandemic excellently and manage to control the outbreak. Therefore, this research studies one country which is New Zealand on how the coronavirus disease affects their trading economy. This research consists of five phases of research methodology to be conducted before presenting the final findings. The five phases are dataset collection, data preprocessing, decision tree regressor, apriori algorithm under association rule mining and finally data visualizations. Using decision tree regressor, apriori algorithm and data visualizations for results, the outcomes of the findings show that the trade for New Zealand is not badly affected by the coronavirus pandemic and two association rules that support their economy have been discovered. © 2022, Success Culture Press. All rights reserved.

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)" as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target" network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (), Sini Decoction (), supplemented formule of Maxing Shigan Decoction (), Fuling Baizhu Decoction () and Dachengqi Decoction (). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Mining Association Rules in Commuter Feedback Comments from Facebook of Swiss National Railways (SBB) Using Apriori Algorithm

Nowadays, all kinds of service-based organizations open online feedback possibilities for customers to share their opinion. Swiss National Railways (SBB) uses Facebook to collect commuters' feedback and opinions. These customer feedbacks are highly valuable to make public transportation option more robust and gain trust of the customer. The objective of this study was to find interesting association rules about SBB's commuters pain points. We extracted the publicly available FB visitor comments and applied manual text mining by building categories and subcategories on the extracted data. We then applied Apriori algorithm and built multiple frequent item sets satisfying the minsup criteria. Interesting association rules were found. These rules have shown that late trains during rush hours, deleted but not replaced connections on the timetable due to SBB's timetable optimization, inflexibility of fines due to unsuccessful ticket purchase, led to highly customer discontent. Additionally, a considerable amount of dissatisfaction was related to the policy of SBB during the initial lockdown of the Covid-19 pandemic. Commuters were often complaining about lack of efficient and effective measurements from SBB when other passengers were not following Covid-19 rules like public distancing and were not wearing protective masks. Such rules are extremely useful for SBB to better adjust its service and to be better prepared by future pandemics. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia.

A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients' data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients' data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients' data.

WiFi-Crowd Spy: A novel crowd-counting system

Currently, there is a requirement in many countries to keep public and work spaces safe due to COVID-19. In fact, indoor spaces must be monitored to control the allowed capacity, which can vary depending on the alert level of a city at a given time. This has motivated some researchers to investigate several technologies to implement methods and strategies to enable the reopening of these spaces in a safe manner. In this paper, we propose a crowd counting detection system that this paper, we propose a crowd counting detection system that addresses the problem of controlling the indoor capacity of offices inside buildings. The proposed solution uses an existing communication technology such as WiFi in order to determine the crowd counting for the indoor environment. In particular, the existing infrastructure consists of two Wireless LAN Controllers (WLC) and several APs deployed in a building, which allows us to estimate the number of people based on the access to Wireless Access Points (APs). Thus, the proposed system takes into account when a mobile device connects/disconnects to the AP to increase or decrease the number of people in a particular office and sends the respective alert to the system administrator when this capacity is about to be exceeded or already surpassed. © 2022 IEEE.

Novel Approach for Predicting COVID-19 Symptoms using ARM based APRIORI Algorithm

Based on several pre-defined standard symptoms, a model that can determine the coronavirus illness as positive is developed. Guidelines for these symptoms have been issued by the World Health Organization (WHO) and India's Ministry of Health and Family Welfare. In this model the various symptoms of the illnesses is given to the system. It allows users to discuss their symptoms, with the algorithm predicting a condition based on factual information. This factual information is then evaluated using the ARM based Apriori algorithm to get the most accurate results. Other conventional models such as Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forests (RF) are considered and have analyzed the predictions and have found that the proposed algorithm predicts a higher accuracy score. © 2022 IEEE.

Acupoint selection rules for acupoint application in lung diseases in the post-epidemic era

Objective: To summarize the rules of acupoint selection of acupoint application to prevent and treat lung diseases under the background the post-epidemic era using data-mining technology. Method: The CNKI, Wanfang database, and VIP database were searched for clinical study articles on lung diseases treated by acupoint application published in the past 5 years. Data-eligible papers were extracted to establish a database of acupoint application for lung disease using Microsoft Excel 2019, with the goal of analyzing the frequency of acupoints, acupoint-meridian association, acupoint-location association, specific acupoint frequency, and cluster analysis. Association rules, consisting of acupoints with an application frequency of ≥ 10, were devised by the Apriori algorithm to explore the correlation between acupoint groups and to analyze the rules of the compatibility of acupoint prescriptions. Results: A total of 229 eligible papers met our inclusion criteria. Forty-seven acupoints were applied, for a total frequency of acupoints of 1,035 times. Among these, acupoints for lung diseases were primarily distributed in the back-and-waist and chest-and-abdomen areas. From the analysis of the association rules, we obtained four groups of acupoint association rules based on acupoint clusters with a frequency ≥ 10 and found that Feishu (BL 13), Tiantu (CV 22), Dazhui (GV 14), Dingchuan (EX-B1), and Danzhong (CV 17) constitute the core acupoints of prescriptions for clinical acupoint application to prevent and treat lung diseases. Conclusion: It is clearly shown that the core acupoints are relatively concentrated and that the selected acupoints were mainly locally selected, which could be a matching reference for the long-term prevention and treatment of lung diseases, including COVID-19.