Abstract The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured
four tracks. The first of these was the de-identification track focused on identifying protected …

Objectives: Implementation of Electronic Health Record (EHR) systems continues to expand.
The massive number of patient encounters results in high amounts of stored data …

Automated regulatory compliance checking requires automated extraction of requirements
from regulatory textual documents and their formalization in a computer-processable rule …

De-identification, identifying information from data, such as protected health information
(PHI) present in clinical data, is a critical step to enable data to be shared or published. The …

Health big data has already been the most important big data for its serious privacy
disclosure concerns and huge potential value of secondary use. Measurements must be …

Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

Natural language processing (NLP) is crucial for advancing healthcare because it is needed
to transform relevant information locked in text into structured data that can be used by …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …

There is a great and growing need to ascertain what exactly is the state of a patient, in terms
of disease progression, actual care practices, pathology, adverse events, and much more …

This paper presents a natural language processing (NLP) system that was designed to
participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify …