TMS 2023 BACK TO BUSINESS AT TMS2023

With nearly 4,500 attendees gathered in San Diego CA, the TMS 2023 Annual Meeting & Exhibition (TMS2023) was the fourth best-attended meeting in TMS history, marking a return to business as usual (more or less) after two decidedly unusual years for the Society's biggest event. By comparison, approximately 2,600 individuals came together in person for TMS2022 in Anaheim CA. One year earlier, TMS2021--held as a fully virtual conference--attracted 2,967 attendees from around the world. This year's event, held Mar 19-23 in one of TMS's most popular meeting locations, brought the conference back closer to its pre-COVID participation numbers. The last time TMS met in San Diego was in 2020 (shortly before widespread pandemic shutdowns began) when more than 4,600 individuals came together for the largest meeting in the Society's history.

Analyzing the research trends of COVID-19 using topic modeling approach

PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach.

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

Integrating deep learning algorithms for the lung segmentation and body-part-specific anatomical classification with Medical Imaging and Data Resource Center (MIDRC)

Medical Imaging and Data Resource Center (MIDRC) has been built to support AI-based research in response to the COVID-19 pandemic. One of the main goals of MIDRC is to make data collected in the repository ready for AI analysis. Due to data heterogeneity, there is a need to standardize data and make data-mining easier. Our study aims to stratify imaging data according to underlying anatomy using open-source image processing tools. The experiments were performed using Google Colaboratory on computed tomography (CT) imaging data available from the MIDRC. We adopted the existing open-source tools to process CT series (N=389) to define the image sub-volumes according to body part classification, and additionally identified series slices containing specific anatomic landmarks. Cases with automatically identified chest regions (N=369) were then processed to automatically segment the lungs. In order to assess the accuracy of segmentation, we performed outlier analysis using 3D shape radiomics features extracted from the left and right lungs. Standardized DICOM objects were created to store the resulting segmentations, regions, landmarks and radiomics features. We demonstrated that the MIDRC chest CT collections can be enriched using open-source analysis tools and that data available in MIDRC can be further used to evaluate the robustness of publicly available tools. © 2023 SPIE.

HarDNet and Dual-Code Attention Mechanism based model for medical images segmentation

During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients’quality of life. Author

Impact of e-Learning in Education Sector: A Sentiment Analysis View

The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy. © 2022 IEEE.

Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets

The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author's stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection. © 2023 ACM.

ASSOCIATION RULE MINING FOR PREDICTION OF COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.

Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach

Emotion classification has become a valuable tool in analyzing text and emotions people express in response to events or crises, particularly on social media and other online platforms. The recent news about monkeypox highlighted various emotions individuals felt during the outbreak. People’s opinions and concerns have been very different based on their awareness and understanding of the disease. Although there have been studies on monkeypox, emotion classification related to this virus has not been considered. As a result, this study aims to analyze the emotions individual expressed on social media posts related to the monkeypox disease. Our goal is to provide real-time information and identify critical concerns about the disease. To conduct our analysis, first, we extract and preprocess 800,000 datasets and then use NRCLexicon, a Python library, to predict and measure the emotional significance of each text. Secondly, we develop deep learning models based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and the combination of Convolutional Neural Networks and Long Short-Term Memory (CLSTM) for emotion classification. We use SMOTE (Synthetic Minority Oversampling Technique) and Random Undersampling techniques to address the class imbalance in our training dataset. The results of our study revealed that the CNN model achieved the highest performance with an accuracy of 96%. Overall, emotion classification on the monkeypox dataset can be a powerful tool for improving our understanding of the disease. The findings of this study will help develop effective interventions and improve public health. Author

DIAGNOSIS OF MEDICAL CONDITIONS IN PATIENTS USING MACHINE LEARNING MODELS

A variety of patient care and intelligent health systems can benefit from the implementation of artificial intelligence as a tool to aid caregivers. Machine learning and deep learning are two types of AI that are increasingly being used in the medical industry. Artificial intelligence methods require a large amount of clinical data from a range of imaging modalities for correct disease diagnosis. In addition, AI has greatly enhanced the quality of hospital stays, allowing patients to be released sooner and complete their recoveries at home. This article aims to provide the information on the field of AI subset i.e., machine learning-based disease detection with information that will aid them in making better decision making. This helps the researchers to classify the medical conditions in patients with a prominent dataset.

Evolution of the Perceived Impact of the Pandemic on University Students

The Coronavirus pandemic has taken the world hostage. All aspects of society have been affected, including the education system with the closure of universities and the adoption of abrupt measures to continue offering university programs virtually. Unexpectedly, the difficult situation has continued until at least December 2021. This paper studies the evolution of the perceived impact of the pandemic on students over four semesters, from Winter 2020 to Fall 2021. A survey conducted at the end of each semester captured the evolution of the impact felt by students. Using Text Mining and Sentiment Analysis, per semester, per gender and per age category, the progression of certain sentiments was identified. The study reveals that the professor's attitude and support was a key element at the beginning of the pandemic and for many, it has been a good learning experience overall. The loss of direct/in person communication has been strongly felt and it got worse as time progresses. The level of negative comments seems to decrease over time for Female students, while for Male students, it tends to increase. Students from different age groups also reacted differently. Students in the most prevalent age group from age 25 to 30 show at first a decline in the proportion of negative comments followed by an increase, while older students from the 30 to 35 age group have a steady decrease of negativity. © 2023 by the authors.

Contextual Market Basket Analysis during Covid-19

One form of Data Mining application to analyze Market Basket Analysis. Market Basket Analysis helps identify buying patterns formed from concurrent transactions. One of the problems with Market Basket Analysis is that customer needs vary according to season and time of day, especially during this covid-19 season. For this purpose, by using the Artificial Neural Network (ANN) Approach that is connected to Market Basket Analysis, it can analyze and compare purchasing patterns and can identify rules that were formed before and after covid-19;several rule changes were found due to changes in people's behavior patterns. [ FROM AUTHOR] Copyright of Journal of Social Science (2720-9938) is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

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.

Mining association rules for classification using frequent generator itemsets in arules package

Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we propose an extension of arules package by adding the option of mining frequent generator itemsets. We discuss in detail how generators can be used for a classification task through an application example in relation with COVID-19.

Current status and hotspot of Lonicera Japonica Flos research based on bibliometrics.

In order to comprehensively understand the research hotspots and development trends of Lonicera Japonica Flos in the past 20 years, and to provide intuitive data reference and objective opinions and suggestions for subsequent related research in this field, this study collected 8 871 Chinese literature and 311 English literature related to Lonicera Japonica Flos research in the core collection databases of Wanfang Data), CNKI and Web of Science (WOS) from 2002 to 2021, and conducted bibliometric and visual analysis using vosviewer. The results showed that the research on the active components of Lonicera Japonica Flos based on phenolic acid components, the research on the mechanism of novel coronavirus pneumonia based on data mining and molecular docking technology, and the pharmacological research on the anti-inflammatory and antiviral properties of Lonicera Japonica Flos are the three hot research directions in the may become the future research direction. In this paper, we analyze the research on Lonicera Japonica Flos from five aspects: active ingredients, research methods, formulation and preparation, pharmacological effects and clinical applications, aiming to reveal the research hotspots, frontiers and development trends in this field and provide predictions and references for future research.Copyright © Drug Evaluation Research 2022.

Drug discovery for coronavirus disease 2019 based on text mining and biomedical databases.

Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway.Copyright © 2022 Tianjin Press of Chinese Herbal Medicines. All Rights Reserved.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

Economic policy uncertainty, Covid-19 and corporate investment: Evidence from the gold mining industry

Mining is a capital-intensive sector that requires substantial upfront investments and continuous capital expenditure to sustain and improve production. This study investigates the impact of Economic Policy Uncertainty (EPU) on the investment decisions of the top 5 gold mining countries, namely Australia, China, Russia, the USA, and Canada, with a focus on the COVID-19 Pandemic. Using a two-step generalized method of moments, we analyze data from 333 gold mining companies from 2006 to 2021. Our results demonstrate that the EPU index has a negative effect on the investment decisions of gold mining companies during the COVID-19 Pandemic. We also utilize quantile regression analysis, which shows that the estimated coefficients for the low and high quantiles are significant. Our study reveals that during periods of uncertainty, gold mining companies tend to be risk-averse, which subsequently dampens investment projects. Furthermore, the capital-intensive nature of the gold mining sector renders companies to be more vulnerable to economic conditions. These findings have significant policy implications for investors, portfolio managers, and policymakers, which will be discussed in the conclusion section.

WHAT DO USERS OF RHEUMATOID ARTHRITIS ONLINE FORUMS TALK ABOUT? APPLYING A DEEP LEARNING APPROACH TO UNCOVER COMMON THEMES

BackgroundSocial media platforms have become a vital resource for individuals seeking information and support regarding health issues, including rheumatoid arthritis (RA). As such, the content generated on these platforms represents a valuable source of data for gaining insight into patients' perspectives on RA. However, previous research in this area has primarily relied on qualitative analyses of small sample sizes, limiting the ability to extract meaningful insights from social media content related to RA. With the advancement of machine learning techniques, it is now possible to analyze and extract insights from large volumes of social media posts related to RA.ObjectivesThe purpose of this study was to identify the most common topics discussed in a large dataset of submissions about RA on Reddit, one of the world's largest online forums.MethodsThe data for this study was collected from the two largest Reddit forums ("subreddits”) dedicated to RA, r/rheumatoid arthritis and r/rheumatoid, which have 18.9k and 7.6k members respectively. We retrieved all submissions but excluded responses in our analyses. All deleted or duplicate submissions and those with fewer than 10 words were removed, retaining 11,094 submissions from over 5,000 users for the analysis. To identify common themes, we applied topic modeling, a technique in natural language processing that identifies underlying themes or topics in a collection of documents. We used the Bertopic Python package (Grootendorst, 2022), which employs deep learning techniques to perform the topic modeling.ResultsThe data indicates a significant increase in submissions to the two subreddits, rising from 113 in 2014 to 2892 in 2021 and 1928 in the first 8 months of 2022. Upon analysis, 65 topics were identified, with 4162 submissions (37.5%) remaining unclassified. A topic specifically dedicated to requests to participate in surveys was removed as it did not pertain to the experiences of forum users. Among the remaining topics, the top 10 accounted for 44.90% of all submissions. To better understand each topic, a sample of 10 submissions with the highest probability for that topic were examined (Table 1).Table 1.Top 10 most frequent topicsTopicn of submissionsShare of total*Side effects of methotrexate5268.02%COVID & vaccines4627.04%Mental health4386.68%RF and anti CCP test results3315.04%RA of friends, partners, and close relatives2623.99%Complaints about rheumatologist2123.23%Questions about Humira1882.87%Questions about prednisone1822.77%Diets and RA1752.67%Early symptoms of possible RA1702.59%Exercise and RA1682.56%* After excluding unclassified topicsThree of the ten topics pertained to specific medications - methotrexate, Humira, and prednisone, accounting for 12.71% of the total. The most prevalent topic, at 8.02%, focused on the side effects of methotrexate, with many submissions inquiring about symptoms such as nausea. The second most common topic, at 7.04%, primarily revolved around COVID-19 and related issues, with some pre-COVID vaccine discussions also included. In 2021, COVID-related discussions were the most prevalent topic. The third most frequent topic (6.68% of total), dealt with mental health and the emotional struggles faced by those living with RA.ConclusionThe surge in submissions on Reddit demonstrates its growing popularity as an online forum for discussing topics related to RA. Utilizing deep learning-based topic modeling has proven to be an effective method for extracting meaningful topics from the questions and experiences shared by users. The vast amount of data generated by Reddit, in combination with advanced machine learning techniques, enables both an overview of the various topics discussed and a detailed examination of specific topics. This makes the use of social media data a valuable source of insight into the concerns of RA platform users.Reference[1]Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.Acknowledgements:NIL.Disclosure of InterestsNone Decla ed.

A Predictive Model for Assessing Satisfaction with Online Learning for Higher Education Students During and After COVID-19 Using Data Mining and Machine Learning Techniques: A Case of Jordanian Institutions

Higher education institutions confronted an escalating unexpected pressure to rapidly transform throughout and after the COVID-19 pandemic, by replacing most of the traditional teaching practices with online-based education. Such transformation required institutions to frequently strive for qualities that meet conceptual requirements of traditional education due to its agility and flexibility. The challenge of such electronic learning styles remains in their potential of bringing out many challenges, along with the advantages it has brought to the educational systems and students alike. This research came to shed the light on several factors presented as a predictive model and proposed to contribute to the success or failure in terms of students' satisfaction with online learning. The study took the kingdom of Jordan as a case example country experiencing online education while and after the covid -19 intensive implementation. The study used a dataset collected from a sample of over "300” students using online questionnaires. The questionnaire included "25” attributes mined into the Knime analytics platform. The data was rigorously learned and evaluated by both the "Decision Tree” and "Naive Bayes” algorithms. Subsequently, results revealed that the decision tree classifier outperformed the naïve bayes in the prediction of student satisfaction, additionally, the existence of the sense of community while learning electronically among other reasons had the most contribution to the satisfaction. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)