AIMC Topic: Natural Language Processing

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Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke.

Journal of the American Heart Association
Background Conventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language processing has made it feasible to derive meaning from unstructured data. We aimed to test whether using u...

Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals.

Scientific reports
The United Nations' (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing effo...

Enhancing unsupervised medical entity linking with multi-instance learning.

BMC medical informatics and decision making
BACKGROUND: A lot of medical mentions can be extracted from a huge amount of medical texts. In order to make use of these medical mentions, a prerequisite step is to link those medical mentions to a medical domain knowledge base (KB). This linkage of...

Drug knowledge discovery via multi-task learning and pre-trained models.

BMC medical informatics and decision making
BACKGROUND: Drug repurposing is to find new indications of approved drugs, which is essential for investigating new uses for approved or investigational drug efficiency. The active gene annotation corpus (named AGAC) is annotated by human experts, wh...

Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications.

Computational and mathematical methods in medicine
Word embedding models have recently shown some capability to encode hierarchical information that exists in textual data. However, such models do not explicitly encode the hierarchical structure that exists among words. In this work, we propose a met...

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

Computational and mathematical methods in medicine
A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these o...

Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.

Academic pediatrics
OBJECTIVES: Medically minor but clinically important findings associated with physical child abuse, such as bruises in pre-mobile infants, may be identified by frontline clinicians yet the association of these injuries with child abuse is often not r...

Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care.

International journal of medical informatics
BACKGROUND: Patient centred care necessitates that healthcare experiences and perceived outcomes be considered across all transitions of care. Information encoded within free-text patient experience comments relating to transitions of care are not ca...

A Natural-Language-Processing-Based Procedure for Generating Distractors for Multiple-Choice Questions.

Evaluation & the health professions
One of the most challenging aspects of writing multiple-choice test questions is identifying plausible incorrect response options-i.e., distractors. To help with this task, a procedure is introduced that can mine existing item banks for potential dis...

Natural language processing of head CT reports to identify intracranial mass effect: CTIME algorithm.

The American journal of emergency medicine
BACKGROUND: The Mortality Probability Model (MPM) is used in research and quality improvement to adjust for severity of illness and can also inform triage decisions. However, a limitation for its automated use or application is that it includes the v...