Identifying Similar Questions in the Medical Domain Using a Fine-tuned Siamese-BERT Model

A large number of people search about their health related problems on the web. However, the number of sites with qualified and verified people answering their queries is quite low in comparison to the number of questions being put up. The rate of queries being searched on such sites has further increased due to the COVID-19 pandemic. The main reason people find it difficult to find solutions to their queries is due to ineffective identification of semantically similar questions in the medical domain. For most cases, answers to the queries people ask would be present, the only caveat being the question may be present in a different form than the one asked by the particular user. In this research, we propose a Siamese-based BERT model to detect similar questions using a fine-tuning approach. The network is fine-tuned with medical question-answer pairs and then with question-question pairs to get a better question similarity prediction. © 2022 IEEE.

RoCoFake – A Romanian Covid-19 Fake News Dataset

The Covid-19 pandemic, managed to shed light onto a neglected problem – that of fake news. Even though lockdowns were imposed in most parts of the world, collaboration between researchers across the globe wasn't impeded. Moreover, the lockdown has deprived people of face-to-face interactions and so they shifted towards online communication. This translated into a massive chatting data, which part was true, but fake information also had its share. Therefore, it is of great interest to develop a dataset to try to spot the fake information. RoCoFake comes to address the lack of resources in this domain, by providing a Romanian Covid-19 Fake News dataset, by aggregating various resources available online, like tweets, news titles and fact-checking news sites like factual.ro. This data provides researchers from the medical domain particularly, but not only, with a valuable, open-access data source useful for various research projects. A benchmark for fake news detection is also provided, so that future investigations can compare against our research. Results suggest that even though the dataset is relatively large, improvements can be made by incorporating retweets and comments. © 2022 IEEE.

RoCoFake – A Romanian Covid-19 Fake News Dataset

The Covid-19 pandemic, managed to shed light onto a neglected problem – that of fake news. Even though lockdowns were imposed in most parts of the world, collaboration between researchers across the globe wasn't impeded. Moreover, the lockdown has deprived people of face-to-face interactions and so they shifted towards online communication. This translated into a massive chatting data, which part was true, but fake information also had its share. Therefore, it is of great interest to develop a dataset to try to spot the fake information. RoCoFake comes to address the lack of resources in this domain, by providing a Romanian Covid-19 Fake News dataset, by aggregating various resources available online, like tweets, news titles and fact-checking news sites like factual.ro. This data provides researchers from the medical domain particularly, but not only, with a valuable, open-access data source useful for various research projects. A benchmark for fake news detection is also provided, so that future investigations can compare against our research. Results suggest that even though the dataset is relatively large, improvements can be made by incorporating retweets and comments. © 2022 IEEE.

CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets

Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger to people's health, particularly when false information is shared, for instance recommendations on how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for the medical domain are crucial. While existing fact-checking resources cover COVID-19-related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19-related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19-related (mis)information. The corpus consists of 300 tweets, each annotated with medical named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in moderate inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge indirectly available in pretrained language models. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

An Assessment of the Missing Data Imputation Techniques for COVID-19 Data

In medical domain, the accuracy of the data supplied is critical. Missing values, on the other hand, are a typical occurrence in this sector for a variety of reasons. Most current science concentrates on establishing novel data imputation procedures, but more research on conducting a comprehensive review of existing algorithms is highly desired. Authors have evaluated the performance of four mostly adopted data imputation techniques, i.e., MICE, EM, mean, and KNN on a real-world dataset of COVID-19. KNN is an imputation approach that, according to the findings of the studies, is expected to be a good fit for dealing with missing data in the healthcare industry. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A General Preprocessing Pipeline for Deep Learning on Radiology Images: A COVID-19 Case Study

During the last years, deep learning has been used intensively in medical domain making considerable progress in the diagnosis of diseases from radiology images. This is mainly due to the availability of proven algorithms on several computer vision tasks and the publicly accessible medical datasets. However, most approaches that apply deep learning techniques to radiology images transform these images into a format that conforms with the inputs of conventional learning algorithms and deal with the dataset as a set of 2D independent slices instead of volumetric images. In this work we deal with the problem of preparing DICOM CT scans as 3D images for a machine learning/deep learning architecture. We propose a general preprocessing pipeline composed of four stages for volumetric images processing followed by a 3D CNN architecture for 3D images classification. The proposed pipeline is evaluated through a case study for COVID-19 detection from chest CT scans. Experiment results demonstrate the effectiveness of the proposed preprocessing operations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Analysis on the Performance of Different Categories of CNN Models over COVID-19 Dataset

In recent times, there is an enormous application of machine learning (ML) and deep learning (DL) techniques in various domains. Particularly in the medical domain, DL models must have the potential to aid the medical practitioners for effective decision making. COVID-19 had caused the world to come to a grinding halt nearly 2 years ago when the first case was detected in Wuhan, China. Its ripple effects are still felt to this very day and the problem only seems to be getting worse. Studies show that COVID-19, being a virus, will continue to mutate itself into other forms so long as it isn't completely eradicated. With RT-PCR reports taking up six hours to three days to show the results, it is the need of the hour to come up with a more efficient method to detect this virus. This paper has two-fold objectives, one is to analyse the effect of Convolutional Neural Networks (CNN) models for detecting COVID-19 and another is to explore and analyse the performance of different classes of CNN over COVID-19 dataset. For this research work, a dataset of a total of 6464 images is curated for the purpose of training the various CNN models which includes 2500 images of Normal, 1464 images of COVID-19 and 2500 images of Pneumonia chest x-rays. Various pretrained models are used and compared based on their accuracies. © 2022 IEEE.

A Domain Model for SARS-Co V-2 Rational Vaccine Design to Engineer a Prediction Tool for Binding Affinity and Antigenicity

One of our greatest present challenges are designing vaccines against SARS COV2 and its variants. Rational vaccine design uses computational methods prior to development of a vaccine for testing in animals and humans the latest methods in rational vaccine design use machine learning techniques to predict binding affinity and antigenicity but offer the researchers only isolated stand-Alone tools. A difficulty that software engineers and data scientist face in development of tools for doctors and researchers is their lack of knowledge of the medical domain. This paper presents a set of domain model developed in collaboration between software engineers and a medical researcher in the process of building a tool scientists could use to predict binding affinity and antigenicity of potential designs of SARS COV2 vaccines. A domain model visualizes the real-world entities and their interrelationships, that together define the domain space. This domain model will be useful to other software engineers trying to predict other characteristics of vaccines, such as potential autoimmunity response. © 2021 IEEE.

Modern AI/ML Methods for Healthcare: Opportunities and Challenges

Artificial Intelligence has seen a significant resurgence in the past decade in wide ranging technology and domain areas. Recent progress in digitisation and high influx of biomedical data have led to an unparalleled success of Machine Learning systems in healthcare, which is perceived to be a possible game changer for ‘healthcare to all’. This article gives an account of some of the current applications of AI solutions in the medical domains of diagnosis, prognosis and treatment. The article will also illustrate the implications of AI in the fight against the COVID-19 pandemic. Lastly, the article will summarise the challenges AI currently faces in its wide-scale adoption in the healthcare industry and how they can possibly be dealt with to move towards a more intelligent medical future. This may enable moving towards quality healthcare for all. © 2022, Springer Nature Switzerland AG.