Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. The FIRE 2022 track on Information Retrieval from Microblogs during Disasters (IRMiDis) focused on two important tasks – (i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets. © 2022 Copyright for this paper by its authors.

Classification of Covid-19 Vaccine Opinion and Detection of Symptom-Reporting on Twitter Using Neural Networks

This paper describes my work for the Information Retrieval from Microblogs during Disasters.This track is divided into two sub-tasks. Task 1 is to build an effective classifier for 3-class classification on tweets with respect to the stance reflected towards COVID-19 vaccines.Task 2 is to devise an effective classifier for 4-class classification on tweets that can detect tweets that report someone experiencing COVID-19 symptoms.This paper proposes a classification method based on MLP classifier model.The evaluation shows the performance of our approach, which achieved 0.304 on F-Score in Task 1 and 0.239 on F-Score in Task 2. © 2022 Copyright for this paper by its authors.

The influence of the Covid-19 pandemic on the shunt revision rate

Introduction: During the first year of the Covid-19 pandemic we observed a decrease of our shunt revision rate. In order to investigate a possible correlation with an assumingly lower general infection rate in children in times of lock down and homeschooling, we performed a detailed analysis of our shunt and general pediatric patient population. Method(s): Electronic patient charts retrieval for children admitted for shunt revision or infectious diseases was performed for four time periods (study period April 2020 - March 2021, control periods from three previous years). A detailed analysis of all shunt revision and infectious cases including age and season specific evaluation followed. Possible correlations were investigated. Result(s): A total of 318 shunt revision and 13,919 pediatric cases have been evaluated. The shunt revision rate during the study period was 29% less compared to the average rate of three previous years (p 0.061), the number of pediatric cases with main diagnosis infection dropped significantly (p < 0.05), whereas other pediatric admissions remained stable. Significant age or seasonal influences did not exist. The number of shunt revisions in association with a documented systemic infection or a primary shunt infection dropped significantly during the study period (p<0.05 each). This was not the case for underdrainage, overdrainage (p>0.05 each) or other indications. In general, infections of upper and lower airways, the gastrointestinal and nervous system decreased during the pandemic, urinary infection rates remained stable. Conclusion(s): The decreased shunt revision rate during the first year of the pandemic seems correlate with a decrease of the general infection rate in children and adolescents at the same time. Infectionassociated shunt failures showed a significant decrease during this period compared to previous years.

RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images

In this research, a novel real time approach has been proposed for detection and analysis of Covid19 using chest X-ray images based on a non-iterative deterministic classifier, kernel extreme learning machine (KELM), and a pretrained network ResNet50. The information extraction capability of deep learning and non-iterative deterministic training nature of KELM has been incorporated in the proposed novel fusion model. The binary classification is carried out with a non-iterative deterministic learning based classifier, KELM. Our proposed approach is able to minimize the average testing error up to 2.76 on first dataset, and up to 0.79 on the second one, demonstrating its effectiveness after experimental confirmation. A comparative analysis of the approach with other existing state-of-the-art methods is also presented in this research and the classification performance confirm the advantages and superiority of our novel approach called RES-KELM algorithm.

Planning for a potential paediatric bronchiolitis surge: How Inter-Professional Education (IPE) was used to build a practice-ready clinical team

Introduction: Bronchiolitis is the most common cause for paediatric respiratory hospital admissions in young children in the UK.1 Following the relaxation of international SARS-Cov-2 lockdown measures a potential national surge in cases was predicted, highlighting a need for more collaborative working across core specialities.2 This prompted the use of the principles of Inter-Professional Education (IPE) to prepare and deliver an intervention to improve outcomes for these patients.3 Objectives: * To plan, deliver and evaluate an educational intervention focussed on improving the knowledge, skills and attitudes needed to care for a sick child with bronchiolitis * To utilise the principles of IPE to improve competence and confidence across core specialities involved in the care of a sick child with bronchiolitis Methods: A team from the Adult Intensive Care Unit (AICU) and the Paediatric High Dependency Unit (PHDU) from the Royal Berkshire Hospital in Reading delivered an inter-professional teaching session focussed on caring for the sick child with bronchiolitis. The patient journey was utilised as a framework to teach the core knowledge, skills and attitudes needed to clinically manage a child from the Emergency Department (ED) to the Intensive Care Unit (ICU). Each session included a lecture about bronchiolitis - describing pathophysiology and how to recognise the deteriorating child;a skills and drills tutorial - highlighting the need for weight-based calculations for high flow nasal oxygen, intravenous fluids and drugs;and a practical simulation scenario - focussing on the stabilisation and management of a sick child awaiting retrieval to the Paediatric Intensive Care Unit (PICU). Result(s): 135 healthcare professionals from a range of adult and paediatric disciplines involved in the care of children across the patient journey attended one of fourteen teaching sessions delivered between September to December 2021. Attendees completed a feedback questionnaire. One hundred and twenty-two (90%) reported an extremely high degree of satisfaction overall, with many saying they would recommend the teaching sessions to others. Areas of personal and professional development were highlighted across the following main themes: gaining theoretical knowledge;understanding key equipment;performing drug calculations;preparing for intubation and ventilation;assessing the need for chest physiotherapy techniques;and more collaborative team-working. Free text comments demonstrated that the attendees felt the teaching sessions: built confidence through the sharing of new or improved knowledge and skills;facilitated a safe space to practice using simulation;and provided the opportunity to learn about and from each other. Many of the attendees also commented on areas they wanted to reinforce and further develop in daily clinical practice as a direct result of the sessions. Conclusion(s): On-going evaluation is taking place as the teaching sessions continue throughout the year, facilitating the inclusion of additional inter-professional groups from across core specialities. These sessions have been used as a template for the development of further planned IPE with a more varied range of paediatric clinical cases and presentations. These will continue to build on the transferable knowledge and skills that increase competence and confidence in caring for the sick child whilst developing a more collaborative practice-ready workforce.

CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding

In recent years, COVID-19 has impacted all aspects of human life. As a result, numerous publications relating to this disease have been issued. Due to the massive volume of publications, some retrieval systems have been developed to provide researchers with useful information. In these systems, lexical searching methods are widely used, which raises many issues related to acronyms, synonyms, and rare keywrds. In this paper, we present a hybrid relation retrieval system, CovRelex-SE, based on embeddings to provide high-quality search results. Our system can be accessed through the following URL: https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/. © 2023 Association for Computational Linguistics.

Experience achieved after implementation of the Veno-Venous ExtraCorporeal Membrane Oxygenation(ECMO) retrieval program in the region of Madrid (Spain). Hospital Universitario 12 de Octubre

Objectives: The objetive of this study is to describe the cases trasferred to an ECMO referral;s centre (Hospital Universitario 12 de Octubre, Madrid (Spain)), to investigate characteristics before ECMO and while the patient was on ECMO, to analyse the presence or not of complications secondary to transfer and cannulation and finally to analyse the ICU outcome. Method(s): This is a Prospective study done from November 1st, 2020 to December 31st, 2022. The cases were accepted either for emergency ECMO cannulation in the hospital of origin and retrieval or for conventional transfer. We analysed basic decriptive variables such as male proportion, age, IMC and etiology of ARDS and variables before ECMO such as prone position, duration of non-invasive ventilation, invasive ventilation and ICU leght of stay before ECMO. We recorded ELSO, SOFA and APACHE Severity Scores. We also analysed several variables on ECMO: if prone position on ECMO was done, median days of ECMO and succesfull weaning from ECMO. We also recorded whether there were complications or not in the transfer and cannulation. Finally ICU survival was examined. Result(s): 31 cases were accepted. 22 (71 %) were male. 29 cases were accepted for emergency ECMO cannulation. Median age was 47 years and IMC 31.1. The etiology of SDRA was COVID 19 infection in 23 cases (74% cases). Lenght of non invasive and invasive ventilation before ECMO were 4 days and 3 days respectively and lenght of ICU admission before ECMO was 2 days. Prone position was 1 day and 2 prone sessions were done before ECMO. Severity scores: APACHE 10 , SOFA 4 , ELSO 3 . On ECMO Prone position was done on 15 cases(48.4%) . Median days on ECMO were 13.5 days. Succesfull weaning from ECMO were achieved on 20 cases(61%), 2 cases remain on ECMO. No complications were seen on transfer or cannulation. ICU Survivors were 16(51.6%). Conclusion(s): After 2 years of experience on ECMO retrieval in the region of Madrid ECMO availability was achieved. Our results are similar than ELSO mortality.

Need for Vision: A data-centric approach towards analysing impact of COVID-19

From the beginning of 2020, we saw a rise of a new virus called the Coronavirus and ultimately a pandemic that anyone reading this paper must have been through. With the rise of COVID,many vaccines were found, the global vaccination drive as a result of this naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their support and concerns regarding the vaccines on social media platforms and along with this came up the need of quick identification of people who are experiencing COVID-19 symptoms. So in this paper, an effort has been made to facilitate the understanding of all these complications and help the concerned authorities. With the help of data in the form of Covid-19 tweets, a (machine-learning) classifier has been built which can classify users as per their vaccine related stance and also classify users who have reported their symptoms through tweets. © FIRE 2022: Forum for Information Retrieval Evaluation.

CTC: COVID-19 Tweet Classification using CT-BERT

CTC is my submitted work to the Information Retrieval from Microblogs during Disasters (IRMiDis) Track at the Forum for Information Retrieval Evaluation (FIRE) 2022. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus experience a mild to moderate respiratory illness and recover without requiring special treatment. However, some become seriously ill and require medical attention. Vaccines against coronavirus and prompt reporting of symptoms saved many lives during the pandemic. The analysis of COVID-19-related tweets can provide valuable insights regarding the stance of people toward the new vaccine. It can also help the authorities to plan their strategies based on people's opinions about the vaccine and ensure the effectiveness of vaccination campaigns. Tweets describing symptoms can also aid in identifying high-alert zones and determining quarantine regulations. The IRMiDis track focuses on these COVID-19-related tweets that flooded Twitter. I developed an effective classifier for both Tasks 1 and 2. The evaluation score of my submitted run is reported in terms of accuracy and macro-F1 score. I achieved an accuracy of 0.770, a macro-F1 score of 0.773 in Task 1, and an accuracy of 0.820, a macro-F1 score of 0.746 in Task 2. I enjoyed the first rank among other submissions in both the tasks. © 2022 Copyright for this paper by its authors.

Detecting COVID-19 Vaccine Stance and Symptom Reporting from Tweets using Contextual Embeddings

Determining sentiments of the public with regard to COVID-19 vaccines is crucial for nations to efficiently carry out vaccination drives and spread awareness. Hence, it is a field requiring accurate analysis and captures the interest of many researchers. Microblogs from social media websites such as Twitter sometimes contain colloquial expressions or terminology difficult to interpret making the task a challenging one. In this paper, we propose a method for multi-label text classification for the track of”Information Retrieval from Microblogs during Disasters (IRMiDis)” presented by the”Forum of Information Retrieval Evaluation” in 2022, related to vaccine sentiment among the public and reporting of someone experiencing COVID-19 symptoms. The following methodologies have been utilised: (i) Word2Vec and (ii) BERT, which uses contextual embedding rather than the fixed embedding used by conventional natural language models. For Task 1, the overall F1 score and Accuracy are 0.503 and 0.529, respectively, placing us fourth among all the teams, while for Task 2, they are 0.740 and 0.790, placing us second among all the teams who submitted their work. Our code is openly accessible through GitHub. 1 © 2022 Copyright for this paper by its authors.

Stacked Bi-directional Long Short Term Memory model for Multi-Class Arabic Sentiment Analysis on Covid-19

The world has been affected by the Covid-19 epidemic during the last three years. During that period, most people tended to use social networks, where by searching for topics related to Covid-19, information could be provided to manage decisions by organizations or governments about public health. With the importance of the Arabic language, despite the lack of research targeting it, using Arabic language as a source of data and analyzing it due to the large number of users on social networks gives an impetus to understand people's feelings about the Covid-19 pandemic. One of the challenges facing sentiment analysis in Arabic is the use of dialects. The most common and existing methods used have been quite ineffective as they are oblivious to contextual information and cannot handle long-distance word dependencies. The Iraqi Arabic dialect is one of the Arabic dialects that still suffers from a lack of research in sentiment analysis. In this study, the official page of the Iraqi Ministry of Health on Facebook was used to collect and analysis comments. Word2vec model is incorporated to extract words semantic characteristics. To capture contextual features, Stacked Bi-directional Long Short Term Memory model (Stacked Bi-LSTM) utilizes sequential word vectors derived from the Continuous Bag of Words model. When compared to most common and existing approaches, the proposed method performed well. © 2022 IEEE.

Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains

Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system1 © 2023 Association for Computational Linguistics.

Large Language Model as Unsupervised Health Information Retriever.

Retrieving health information is a task of search for health-related information from a variety of sources. Gathering self-reported health information may help enrich the knowledge body of the disease and its symptoms. We investigated retrieving symptom mentions in COVID-19-related Twitter posts with a pretrained large language model (GPT-3) without providing any examples (zero-shot learning). We introduced a new performance measure of total match (TM) to include exact, partial and semantic matches. Our results show that the zero-shot approach is a powerful method without the need to annotate any data, and it can assist in generating instances for few-shot learning which may achieve better performance.

Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures

Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic features of both constituency parsing (CP) and dependency parsing (DP). The extraction results may also serve as distant supervision for zero-shot model training. Our approach outperforms existing methods from two perspectives: (1) the rules are simple yet effective, and (2) the results are more self-contained. We further propose a numerical information retrieval approach based on QuantityIE to answer analytical queries. Experimental results on information extraction and retrieval demonstrate the effectiveness of QuantityIE in extracting numerical information with high fidelity.

Some key aspects of telemedicine development to support human illness: a systematic review

The aim of this research is to describe the use of telemedicine applied to patients characterised by a particular state of illness, which often drives them toward a frail and chronic status, in a systematic manner. This work employed the Tranfield approach to carry out a systematic literature review (SLR), in order to provide an efficient and high-quality method for identifying and evaluating extensive studies. The methodology was pursued step by step, analysing keywords, topics, journal quality to arrive at a set of relevant open access papers that was analysed in detail. The same papers were compared to each other and then, they were categorised according to significant metrics, also evaluating technologies and methods employed. Through our systematic review we found that most of the patients involved in telemedicine programs agreed with this service model and the clinical results appeared encouraging. Findings suggested that telemedicine services were appreciated by patients, they increased the access to care and could be a better way to face emergencies and pandemics, lowering overall costs and promoting social inclusion.Copyright © 2022 Inderscience Enterprises Ltd.

Searching for evidence in public health emergencies: a white paper of best practices.

Objectives: Information professionals have supported medical providers, administrators and decision-makers, and guideline creators in the COVID-19 response. Searching COVID-19 literature presented new challenges, including the volume and heterogeneity of literature and the proliferation of new information sources, and exposed existing issues in metadata and publishing. An expert panel developed best practices, including recommendations, elaborations, and examples, for searching during public health emergencies. Methods: Project directors and advisors developed core elements from experience and literature. Experts, identified by affiliation with evidence synthesis groups, COVID-19 search experience, and nomination, responded to an online survey to reach consensus on core elements. Expert participants provided written responses to guiding questions. A synthesis of responses provided the foundation for focus group discussions. A writing group then drafted the best practices into a statement. Experts reviewed the statement prior to dissemination. Results: Twelve information professionals contributed to best practice recommendations on six elements: core resources, search strategies, publication types, transparency and reproducibility, collaboration, and conducting research. Underlying principles across recommendations include timeliness, openness, balance, preparedness, and responsiveness. Conclusions: The authors and experts anticipate the recommendations for searching for evidence during public health emergencies will help information specialists, librarians, evidence synthesis groups, researchers, and decision-makers respond to future public health emergencies, including but not limited to disease outbreaks. The recommendations complement existing guidance by addressing concerns specific to emergency response. The statement is intended as a living document. Future revisions should solicit input from a broader community and reflect conclusions of meta-research on COVID-19 and health emergencies.

Proposed information retrieval systems using LDA topic modeling for answer finding of COVID 19 pandemic: A brief survey of approaches and techniques

Understanding public responses to emergencies, including outbreaks of diseases, is necessary and significant. A demonstration of how to separate papers about the virus Covid-19 into different topics using topic modeling techniques in several studies is introduced in this research article. Inthe field of machine learning, topic modeling is a major topic. Though primarily, it is used to build models. It provides a quick and easy way to mine data from unstructured textual data, with samples representing documents.The most extensively utilized subject modeling approaches are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). On the other hand, model creation can be tedious and repetitious, requiring costly and methodical sensitivity analyses to determine the ideal collection of model parameters. Moreover, comparing models frequently require time-consuming subjective opinions. The topic models assign a probability to each word in the vocabulary corpus related to one or more themes (LSA, LDA). Several LDA and LSA models with varied degrees of coherence were generated, and the model with the greatest degree of coherence was selected. This experiment demonstrates that LDA outperforms LSA. © 2023 Author(s).

Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access

During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health planning, and misinformation mitigation. However, creating such an information portal is challenging because 1) domain expertise is required to identify and curate credible and intelligible content, 2) the information needs to be updated promptly in response to the fast-changing environment, and 3) the information should be easily accessible by the general public;which is particularly difficult when most people do not have the domain expertise about the crisis. In this paper, we presented an expert-sourcing framework and created Jennifer, an AI chatbot, which serves as a credible and easy-to-access information portal for individuals during the COVID-19 pandemic. Jennifer was created by a team of over 150 scientists and health professionals around the world, deployed in the real world and answered thousands of user questions about COVID-19. We evaluated Jennifer from two key stakeholders' perspectives, expert volunteers and information seekers. We first interviewed experts who contributed to the collaborative creation of Jennifer to learn about the challenges in the process and opportunities for future improvement. We then conducted an online experiment that examined Jennifer's effectiveness in supporting information seekers in locating COVID-19 information and gaining their trust. We share the key lessons learned and discuss design implications for building expert-sourced and AI-powered information portals, along with the risks and opportunities of misinformation mitigation and beyond. © 2023 Owner/Author.

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review;the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects;it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.

The Relationship Between Students' Flow Experience and Their Behavior Data in Gamified Educational Systems

The number of students using online educational systems is increasing, especially after the growth of the use of this type of system due to the social isolation caused by the Covid-19 pandemic. This situation highlighted the challenge of analyzing the users' experience in this type of system, especially when evaluating more complex experiences, such as the flow experience. One of the most promising innovative alternatives is to use the behavior data logs produced by students in educational systems to analyze their experiences. In this paper, we conducted a study (N = 24) to analyze the relationships between the behavior data logs produced by students when using a gamified educational system and their flow experience during the system usage. Our results contribute to the automatic users' experience analysis in educational systems. © 2022 IEEE Computer Society. All rights reserved.