AIMC Topic: Natural Language Processing

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An automated data verification approach for improving data quality in a clinical registry.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (C...

Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text.

BMC medical informatics and decision making
BACKGROUND: Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction...

Natural Language Processing-Identified Problem Opioid Use and Its Associated Health Care Costs.

Journal of pain & palliative care pharmacotherapy
Use of prescription opioids and problems of abuse and addiction have increased over the past decade. Claims-based studies have documented substantial economic burden of opioid abuse. This study utilized electronic health record (EHR) data to identify...

Automatic Disease Annotation From Radiology Reports Using Artificial Intelligence Implemented by a Recurrent Neural Network.

AJR. American journal of roentgenology
OBJECTIVE: Radiology reports are rich resources for biomedical researchers. Before utilization of radiology reports, experts must manually review these reports to identify the categories. In fact, automatically categorizing electronic medical record ...

A natural language processing algorithm to extract characteristics of subdural hematoma from head CT reports.

Emergency radiology
PURPOSE: Subdural hematoma (SDH) is the most common form of traumatic intracranial hemorrhage, and radiographic characteristics of SDH are predictive of complications and patient outcomes. We created a natural language processing (NLP) algorithm to e...

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

Human genetics
In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intellig...

A neural classification method for supporting the creation of BioVerbNet.

Journal of biomedical semantics
BACKGROUND: VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing an...

Development of a cardiac-centered frailty ontology.

Journal of biomedical semantics
BACKGROUND: A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac ...

An efficient prototype method to identify and correct misspellings in clinical text.

BMC research notes
OBJECTIVE: Misspellings in clinical free text present challenges to natural language processing. With an objective to identify misspellings and their corrections, we developed a prototype spelling analysis method that implements Word2Vec, Levenshtein...