Inferring potential drug indications, for either novel or approved drugs, is a key step in drug
development. Previous computational methods in this domain have focused on either drug …

The combination of recent developments in question-answering research and the
availability of unparalleled resources developed specifically for automatic semantic …

In this study, we evaluate the performance of a Natural Language Processing (NLP)
application designed to extract medical problems from narrative text clinical documents. The …

Background Information extraction is a complex task which is necessary to develop high-
precision information retrieval tools. In this paper, we present the platform MeTAE (Medical …

Information extraction (also known as relation extraction) is generally defined as the
extraction of information about a very focussed type of relationship from free text. Historically …

Background A large-scale, highly accurate, machine-understandable drug-disease
treatment relationship knowledge base is important for computational approaches to drug …

Background and objective As people increasingly engage in online health-seeking behavior
and contribute to health-oriented websites, the volume of medical text authored by patients …

Background The medical problem list is an important part of the electronic medical record in
development in our institution. To serve the functions it is designed for, the problem list has …

This paper presents a Graph Inference retrieval model that integrates structured knowledge
resources, statistical information retrieval methods and inference in a unified framework. Key …

Objective To reliably extract two entity types, symptoms and conditions (SCs), and drugs and
treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from …