AIMC Topic: Natural Language Processing

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TGIN: Document-level event extraction with two-phase graph inference network.

Neural networks : the official journal of the International Neural Network Society
Document-level event extraction aims to extract event records from a whole document that contain numerous entities scattered across multiple sentences. Efficiently modeling the interactions among these entities is crucial. However, previous methods s...

A survey of recent methods for addressing AI fairness and bias in biomedicine.

Journal of biomedical informatics
OBJECTIVES: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recogni...

Prediction of coronary artery bypass graft outcomes using a single surgical note: An artificial intelligence-based prediction model study.

PloS one
BACKGROUND: Healthcare providers currently calculate risk of the composite outcome of morbidity or mortality associated with a coronary artery bypass grafting (CABG) surgery through manual input of variables into a logistic regression-based risk calc...

Exploring the Limits of Artificial Intelligence for Referencing Scientific Articles.

American journal of perinatology
OBJECTIVE:  To evaluate the reliability of three artificial intelligence (AI) chatbots (ChatGPT, Google Bard, and Chatsonic) in generating accurate references from existing obstetric literature.

Ethical and regulatory challenges of large language models in medicine.

The Lancet. Digital health
With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergen...

A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals.

BMC medical informatics and decision making
BACKGROUND: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured...

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Frontiers in public health
INTRODUCTION: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-rel...

Use of natural language processing to uncover racial bias in obstetrical documentation.

Clinical imaging
Natural Language Processing (NLP), a form of Artificial Intelligence, allows free-text based clinical documentation to be integrated in ways that facilitate data analysis, data interpretation and formation of individualized medical and obstetrical ca...

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

Methods (San Diego, Calif.)
In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies c...

Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the ...