The prime purpose of the proposed study is to construct a novel predictive scheme for
assisting in the prognosis of criticality using the MIMIC-III dataset. With the adoption of …

Acronyms are the short forms of phrases that facilitate conveying lengthy sentences in
documents and serve as one of the mainstays of writing. Due to their importance, identifying …

Objective: The present study aims to investigate changes in the frequency of parent-child
contact among Europeans aged 65 years and over within the context of the COVID-19 …

This research proposes a new approach to improve information retrieval systems based on
a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi …

Various deep learning algorithms have been developed to analyze different types of clinical
data including clinical text classification and extracting information from'free text'and so on …

Scientific papers from all disciplines contain many abbreviations and acronyms. In many
cases these acronyms are ambiguous. We present a method to choose the contextual …

Background This article describes a high-recall, high-precision approach for the extraction of
biomedical entities from scientific articles. Method The approach uses a two-stage pipeline …

Patients with cancer, such as breast and colorectal cancer, often experience different
symptoms post-chemotherapy. The symptoms could be fatigue, gastrointestinal (nausea …

There are challenges for analyzing the narrative clinical notes in Electronic Health Records
(EHRs) because of their unstructured nature. Mining the associations between the clinical …

Neural embeddings are a popular set of methods for representing words, phrases or text as
a low dimensional vector (typically 50–500 dimensions). However, it is difficult to interpret …