Automatic document screening of medical literature using word and text embeddings in an active learning setting

An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2

To build a full picture of previous studies on the origins of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), this paper exploits an active learning-based approach to screen scholarly articles about the origins of SARS-CoV-2 from many scientific publications. In more detail, six seed articles were utilized to manually curate 170 relevant articles and 300 nonrelevant articles. Then, an active learning-based approach with three query strategies and three base classifiers is trained to screen the articles about the origins of SARS-CoV-2. Extensive experimental results show that our active learning-based approach outperforms traditional counterparts, and the uncertain sampling query strategy performs best among the three strategies. By manually checking the top 1,000 articles of each base classifier, we ultimately screened 715 unique scholarly articles to create a publicly available peer-reviewed literature corpus, COVID-Origin. This indicates that our approach for screening articles about the origins of SARS-CoV-2 is feasible.

NeuroCORD: A Language Model to Facilitate COVID-19-Associated Neurological Disorder Studies

COVID-19 can lead to multiple severe outcomes including neurological and psychological impacts. However, it is challenging to manually scan hundreds of thousands of COVID-19 articles on a regular basis. To update our knowledge, provide sound science to the public, and communicate effectively, it is critical to have an efficient means of following the most current published data. In this study, we developed a language model to search abstracts using the most advanced artificial intelligence (AI) to accurately retrieve articles on COVID-19-associated neurological disorders. We applied this NeuroCORD model to the largest benchmark dataset of COVID-19, CORD-19. We found that the model developed on the training set yielded 94% prediction accuracy on the test set. This result was subsequently verified by two experts in the field. In addition, when applied to 96,000 non-labeled articles that were published after 2020, the NeuroCORD model accurately identified approximately 3% of them to be relevant for the study of COVID-19-associated neurological disorders, while only 0.5% were retrieved using conventional keyword searching. In conclusion, NeuroCORD provides an opportunity to profile neurological disorders resulting from COVID-19 in a rapid and efficient fashion, and its general framework could be used to study other COVID-19-related emerging health issues.

A Comparative Analysis of Active Learning for Biomedical Text Mining

An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called active learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL)-based strategies on three unique biomedical datasets. We investigated random sampling (RS), least confidence (LC), informative diversity and density (IDD), margin and maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Furthermore, pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling.