The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …

Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

The digitization of healthcare has facilitated the sharing and re-using of medical data but has
also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability …

Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …

Abstract With Artificial Intelligence (AI) increasingly permeating various aspects of society,
including healthcare, the adoption of the Transformers neural network architecture is rapidly …

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

The purpose of this article is to see how machine learning (ML) algorithms and applications
are used in the COVID-19 inquiry and for other purposes. The available traditional methods …

A large number of stock reviews are available on the Internet. Sentiment analysis of stock
reviews has strong significance in research on the financial market. Due to the lack of a …

Abstract Background Electronic health records (EHRs) are a valuable resource for data-
driven medical research. However, the presence of protected health information (PHI) …

This paper investigates the ability of multilingual BERT (mBERT) language model to transfer
syntactic knowledge cross-lingually, verifying if and to which extent syntactic dependency …