AIMC Topic: Natural Language Processing

Clear Filters Showing 3501 to 3510 of 3983 articles

Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.

Classifying Pseudogout Using Machine Learning Approaches With Electronic Health Record Data.

Arthritis care & research
OBJECTIVE: Identifying pseudogout in large data sets is difficult due to its episodic nature and a lack of billing codes specific to this acute subtype of calcium pyrophosphate (CPP) deposition disease. The objective of this study was to evaluate a n...

[Artificial intelligence based Chinese clinical trials eligibility criteria classification].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility cri...

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.

Proceedings of the National Academy of Sciences of the United States of America
Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get bet...

Naïve Definitions of Action and Inaction: A Study of Free Associations Using Natural Language Processing and Top-Down Coding.

Psicothema
BACKGROUND: Even though the terms "action" and "inaction" have been used to describe objects of attitudes, behaviors, and goals, the meaning of action and inaction for lay people has not been investigated.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.

Bioinformatics (Oxford, England)
MOTIVATION: The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large am...

Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decisi...

Development of a predictive model for retention in HIV care using natural language processing of clinical notes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Adherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied n...

Integrity of clinical information in radiology reports documenting pulmonary nodules.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Quantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports and assess impact on making follow-up recommendations.