Learning Topical Structured Interfaces from Medical Research Literature

Accessing large-scale structured datasets such as WDC or CORD-191 is very challenging. Even if one topic (e.g. COVID-19 vaccine efficacy) is of interest, all topical tables in different sources/papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate a scalable Meta-profiler system, capable of constructing a structured standardized interface to a topic of interest in large-scale (semi-)structured datasets. This interface, that we call Meta-profile represents a multi-dimensional meta-data summary for a selected topic of interest, accumulating all differently structured representations of the topical tables in the dataset. Such Meta-profiles can be used as a rich visualization as well as a robust structural query interface simplifying access to large-scale (semi-)structured data for different user segments, such as data scientists and end users. © 2023 Owner/Author.

PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit

The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model. © 2022 IEEE.

A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.

Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case

This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature;(2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction;(3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects. © 2022 IEEE.

Information retrieval and extraction on COVID-19 clinical articles using graph community detection and Bio-BERT embeddings

In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module. © ACL 2020.All right reserved.

Curriculum Contrastive Learning for COVID-19 FAQ Retrieval

Medical Frequently Asked Question (FAQ) retrieval aims to find the most relevant question-answer pairs for a given user query, which is of great significance for enhancing people medical health awareness and knowledge. However, due to medical data privacy and labor-intensive labeling, there is a lack of large-scale question-matching training datasets. Previous methods directly use the collected question-answer pairs on search engines to train retrieval models, which achieved poor performance. Inspired by recent advances in contrastive learning, we propose a novel contrastive curriculum learning framework for modeling user medical queries. First, we design different data augmentation methods to generate positive samples and different types of negative samples. Second, we propose a curriculum learning strategy that associates difficulty levels with negative samples. Through a contrastive learning process from easy to hard, our method achieves excellent results on two medical datasets. © 2022 IEEE.

A Comparative Study of Question Answering over Knowledge Bases

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at https://github.com/tamlhp/kbqa.

Curriculum Contrastive Learning for COVID-19 FAQ Retrieval

Medical Frequently Asked Question (FAQ) retrieval aims to find the most relevant question-answer pairs for a given user query, which is of great significance for enhancing people medical health awareness and knowledge. However, due to medical data privacy and labor-intensive labeling, there is a lack of large-scale question-matching training datasets. Previous methods directly use the collected question-answer pairs on search engines to train retrieval models, which achieved poor performance. Inspired by recent advances in contrastive learning, we propose a novel contrastive curriculum learning framework for modeling user medical queries. First, we design different data augmentation methods to generate positive samples and different types of negative samples. Second, we propose a curriculum learning strategy that associates difficulty levels with negative samples. Through a contrastive learning process from easy to hard, our method achieves excellent results on two medical datasets. © 2022 IEEE.

An AI-Based Medical Chatbot Model for Infectious Disease Prediction

The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.

UMLS-Based Question-Answering Approach for Automatic Initial Frailty Assessment

Frailty is the most problematic multidimensional geriatric syndrome among elderly population that leads to poor quality of life and increased risk of death. Adverse effects include an increased risk of hospitalisation and institutionalisation, poorer outcomes of post-hospitalisation, and higher mortality rates. A questionnaire-based frailty assessment is an effective way to achieve early diagnosis of frailty. However, most of the existing frailty assessment tools require face-to-face consultation. For elderly patients living in rural areas are more likely to struggle to access healthcare than a patient living in an urban or suburban area, and they have higher chance of catching diseases due to frequent hospital visits as most of them are vulnerable due to being immunocompromised. An automatic initial frailty assessment approach can minimise the impact of mentioned disadvantages and save clinical resources by avoiding unnecessary manual assessments. The objective of this paper is to propose an automatic initial frailty assessment approach which can quickly identify potential patients that require further frailty assessment by using patient's relevant clinical notes to answer Tillburg Frailty Indicator (TFI) questionnaire automatically. A phrase-based query expansion method is proposed to identify the most relevant phrases to the frailty assessment questionnaire based on UMLS ontology. The research shows the advantages of using UMLS based concepts as features in automatic initial frailty assessment using clinical notes. The research enables clinician to assess frailty automatically using medical data, reduces the frequency of face-to-face consultations and hospital visits, which is extremely beneficial during unusual or unexpected times such as COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Comparative Study of Question Answering over Knowledge Bases

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at https://github.com/tamlhp/kbqa. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Thai Question-Answering System Using Pattem-Matching Approach

Teaching concepts in Thailand's universities have abruptly changed, due to the advancement of the COVID-19 pandemic, including changes in classroom to online formats, as well as administrative difficulties. The research herein, therefore, addresses these concerns, presenting a Thai question-answering system using the pattern-matching approach. Our case study covers course information, teaching timetable, teacher schedule, and course supplements. We classified the questions into six categories according to type and acknowledged typical expressions which matched to question patterns. We use RegEx® to match a defined pattern. When a response did not match, we used word embedding to transform the question into a vector and then calculated the cosine similarity to identify the most similar pattern. The system can then generate a corresponding SQL command to query the answer from the database. We evaluated the accuracy of the proposed system with the collected data resulted in an accuracy rate of 82%. © 2022 IEEE.

Design of epidemic detection system in railway station based on FPGA

Since the outbreak of COVID-19 in 2020, wearing masks and displaying green codes in and out of public places has become a habit. The identity verification of the existing railway station is mainly based on face recognition. Due to the outbreak and persistence of the epidemic, people often need to call out the green code on their mobile phones for staff verification, which takes a lot of time. At the same time, the existing face recognition equipment requires the inbound personnel to take off their masks, which also increases the infection risk of the inbound personnel. In order to reduce the above infection risk and speed up people's entry and exit speed, we have designed a system that can identify people wearing masks and judge whether they are confirmed or suspected cases at the same time. Firstly, the system measures the passenger's body temperature through the infrared temperature measurement module, carries out face detection and recognition at the same time, and queries the recognition results in the database to judge whether the passenger is diagnosed or in close contact. When the passenger is normal, it is allowed to pass, otherwise it is not allowed to pass, and updates the relevant data in the cloud database. The system uses Yolo algorithm as the face detection algorithm, and then carries out face recognition through FaceNet network, so as to judge its identity and query the relevant information of the person in the cloud database. After testing, the iterative loss rate of the system is basically below 0.1 and the accuracy is basically stable above 99%. Considering that we need to use it on embedded devices and the amount of calculation operation of deep learning algorithm is large, and FPGA can well build circuits according to the needs of the model because of its reconfigurability, and FPGA can realize hardware acceleration because it can run in parallel, so we finally choose to deploy the model to FPGA to complete face recognition. © 2022 IEEE.

Overview of the 36th International Conference, 36th Eg-MRS 202

The overview describes the main directions and results of the 36th Eg-MRS International Conference, 36th Eg-MRS-2022 held in Cairo, Egypt, 24-25 September 2022. It gives the details about the participants and the proceedings.The Volume contains Proceedings of the 36th Eg-MRS International Conference, Eg-MRS-2022:Modernization, Innovations and Progress” which was held in Cairo, Egypt in September 24-25, 2022.. The Conference held online using Zoom due to COVID-19.The purpose of the Conference is to share the results and prospects of the achievements in using advanced scientific, innovative and information technologies in materials sciences and its applications.In fact, this is the third year of publishing a special issue of Eg-MRS international conferences under the umbrella of IOP material sciences and engineering. The first one was in November 2019;Hurghada, Egypt [1]. The second and third was in August 2020 & July 2022 which was online due to COVID-19 [2,3 ]All conference organizers/editors are required to declare details about their peer review. Therefore, please provide the following information:• Type of peer review: Single-blind / Double-blind / Triple-blind / Open / Other (please describe) Double-blind• Conference submission management system: Online: https://cutt.us/mo8G5• Number of submissions received: 50• Number of submissions sent for review: 30• Number of submissions accepted: 11• Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received × 100): 22%• Average number of reviews per paper: 3• Total number of reviewers involved: 20• Any additional info on review process: No• Contact person for queries: Prof Arafa H.Aly;arafaaly@aucegypt.eduPlease submit this form along with the rest of your files on the submission date written in your publishing agreement.The information you provide will be published as part of your proceedings.

CheetahKG: A Demonstration for Core-based Top-k Frequent Pattern Discovery on Knowledge Graphs

Knowledge graphs capture the complex relationships among various entities, which can be found in various real world applications, e.g., Amazon product graph, Freebase, and COVID-19. To facilitate the knowledge graph analytical tasks, a system that supports interactive and efficient query processing is always in demand. In this demonstration, we develop a prototype system, CheetahKG, that embeds with our state-of-the-art query processing engine for the top-k frequent pattern discovery. Such discovered patterns can be used for two purposes, (i) identifying related patterns and (ii) guiding knowledge exploration. In the demonstration sessions, the attendees will be invited to test the efficiency and effectiveness of the query engine and use the discovered patterns to analyze knowledge graphs on CheetahKG. © 2022 IEEE.

Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering

This paper presents Macquarie University’s participation to the two most recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task B (BioASQ10b), Phase B. In these tasks, participating systems are expected to generate complex answers to biomedical questions, where the answers may contain more than one sentence. We apply query-focused extractive summarisation techniques. In particular, we follow a sentence classification-based approach that scores each candidate sentence associated to a question, and the n highest-scoring sentences are returned as the answer. The Synergy Task corresponds to an end-to-end system that requires document selection, snippet selection, and finding the final answer, but it has very limited training data. For the Synergy task, we selected the candidate sentences following two phases: document retrieval and snippet retrieval, and the final answer was found by using a DistilBERT/ALBERT classifier that had been trained on the training data of BioASQ9b. Document retrieval was achieved as a standard search over the CORD-19 data using the search API provided by the BioASQ organisers, and snippet retrieval was achieved by re-ranking the sentences of the top retrieved documents, using the cosine similarity of the question and candidate sentence. We observed that vectors represented via sBERT have an edge over tf.idf. BioASQ10b Phase B focuses on finding the specific answers to biomedical questions. For this task, we followed a data-centric approach. We hypothesised that the training data of the first BioASQ years might be biased and we experimented with different subsets of the training data. We observed an improvement of results when the system was trained on the second half of the BioASQ10b training data. © 2022 Copyright for this paper by its authors.

Designing a Sustainable Data Structure for Exploring Student Well-Being Patterns in Albemarle County Public Schools Throughout COVID-19

Student well-being has been affected by the COVID-19 pandemic. Albemarle County Public Schools (ACPS) has collected a significant and varied amount of K-12 student data throughout COVID-19. Researchers seek to utilize the student data to drive evidence-based policy changes with regard to ACPS student well-being. A structured data system for performing school-related research associated with the well-being of students throughout the pandemic does not exist. We have designed a sustainable, relational data structure for data consolidation and to advance the ongoing research initiatives related to COVID-19 student well-being in collaboration with ACPS. The data structure aims to play an important role in promoting student well-being policies through simplifying data collection, enhancing analysis, and acting as an ongoing tool that can support future phases of research. The design architecture includes a relational database populated with de-identified student data to be hosted in the cloud. Design implementation includes data cleaning, data preprocessing, populating the database, and querying data for validation. Specialized queries are utilized to answer the early questions posed to the data. Validation testing is performed to confirm the database is working as expected. Details of the data pipeline, validation, best data practices, and database design are discussed in the paper. © 2022 IEEE.

What a publication tells you - benefits of narrative information access in digital libraries

Knowledge bases allow effective access paths in digital libraries. Here users can specify their information need as graph patterns for precise searches and structured overviews (by allowing variables in queries). But especially when considering textual sources that contain narrative information, i.e., short stories of interest, harvesting statements from them to construct knowledge bases may be a serious threat to the statements' validity. A piece of information, originally stated in a coherent line of arguments, could be used in a knowledge base query processing without considering its vital context conditions. And this can lead to invalid results. That is why we argue to move towards narrative information access by considering contexts in the query processing step. In this way digital libraries can allow users to query for narrative information and supply them with valid answers. In this paper we define narrative information access, demonstrate its benefits for Covid 19 related questions, and argue on the generalizability for other domains such as political sciences. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

An Approach and Implementation for Knowledge Graph Construction and QA System

In this paper, we investigate and propose a knowledge graph-based method and implementation of the question-and-answer (QA) system for COVID-19 cases imported from abroad. It mainly analyzes and organizes the knowledge graph construction methods based on knowledge acquisition and visualization. In addition, this paper implements the knowledge graph-based QA system by training term frequency-inverse document frequency (TF-IDF) model and Bidirectional Long Short-Term Memory + Conditional Random Field (Bi-LSTM+CRF) model as well as Cypher query statements using the graph database Neo4j. Finally, the visual intelligent interface of the QA system is designed to meet user requirements and realize the function of accurate QA. © 2021 IEEE.

Forward privacy multikeyword ranked search over encrypted database

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade-off in efficiency, security, and functionality. Recent work proposes a file-injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well-chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the (Formula presented.) rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID-19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient. © 2022 Wiley Periodicals LLC.