Background Text mining and natural language processing of clinical text, such as notes from
electronic health records, requires specific consideration of the specialized characteristics of …

Medical information extraction consists of a group of natural language processing (NLP)
tasks, which collaboratively convert clinical text to pre-defined structured formats. This is a …

Longstanding diabetes mellitus is today known as the primary reason for kidney failure in
the patients having that condition. While the prior research has studied the confounding role …

Objectives A substantial portion of the data contained in Electronic Health Records (EHR) is
unstructured, often appearing as free text. This format restricts its potential utility in clinical …

Machine learning has demonstrated superior performance in solving many problems in
various fields of medicine compared to non-machine learning approaches. The aim of this …

This work focuses on the creation of a system to detect negated medical entities in electronic
health records (EHRs) written in Spanish. The importance of this task rests on the influence …

Constructing effective models for detecting, reducing, and/or preventing adverse events is
very important in domains such as aviation safety, healthcare, drug administration, and war …

Multisensor information fusion brings challenges such as data heterogeneity, source
precision, and the merger of uncertainties that impact the quality of classifiers. A widely used …

Abstract Background The US Food and Drug Administration (FDA) collects and retains
several data sets on post-market drugs and associated adverse events (AEs). The FDA …

Information extraction and knowledge discovery regarding adverse drug reaction (ADR)
from large‐scale clinical texts are very useful and needy processes. Two major difficulties of …