Automated medical coding, an essential task for healthcare operation and delivery, makes
unstructured data manageable by predicting medical codes from clinical documents. Recent …

Background: In response to the intricate language, specialized terminology outside
everyday life, and the frequent presence of abbreviations and acronyms inherent in health …

In recent years, Transformer-based models such as the Switch Transformer have achieved
remarkable results in natural language processing tasks. However, these models are often …

Language Models (LMs) have shown remarkable potential in healthcare applications, yet
their widespread adoption faces challenges in achieving consistent performance across …

We detail a deep learning approach based on the transformer architecture for performing
fake news detection. The proposed approach is composed of a deep learning network which …

We leverage generative NLP-based models, specifically Transformer-Based models, for
multi-horizon univariate and multivariate power consumption forecasting. We apply our …

We present a novel approach to automating the identification of risk factors for diseases from
medical literature, leveraging pre-trained models in the bio-medical domain, while tuning …

The detection of contaminants in several environments (eg, air, water, sewage systems) is of
paramount importance to protect people and predict possible dangerous circumstances …

Artificial Intelligence in the healthcare sector is becoming increasingly essential to extract
huge texts for decision-making. Extraction of clinical data is a fundamental task in Medical …

Medical dialogue generation relies on natural language generation techniques to enable
online medical consultations. Recently, the widespread adoption of large-scale models in …