AIMC Topic: Natural Language Processing

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Semantic-guided attention and adaptive gating for document-level relation extraction.

Scientific reports
In natural language processing, document-level relation extraction is a complex task that aims to predict the relationships among entities by capturing contextual interactions from an unstructured document. Existing graph- and transformer-based model...

NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.

Medical & biological engineering & computing
Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer ...

Semi-automated title-abstract screening using natural language processing and machine learning.

Systematic reviews
BACKGROUND: Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particula...

Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.

European radiology
PURPOSE: Medical reports, governed by HIPAA regulations, contain personal health information (PHI), restricting secondary data use. Utilizing natural language processing (NLP) and large language models (LLM), we sought to employ publicly available me...

Qualitatively Assessing ChatGPT Responses to Frequently Asked Questions Regarding Sexually Transmitted Diseases.

Sexually transmitted diseases
BACKGROUND: ChatGPT, a large language model artificial intelligence platform that uses natural language processing, has seen its implementation across a number of sectors, notably in health care. However, there remains limited understanding regarding...

Developing healthcare language model embedding spaces.

Artificial intelligence in medicine
Pre-trained Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP) tasks, but often struggle when applied to specialised domains such as healthcare. The traditional approach of pre-training on large datasets followed by ta...

Clinical Prompt Learning With Frozen Language Models.

IEEE transactions on neural networks and learning systems
When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work sugg...

Biologically Plausible Sparse Temporal Word Representations.

IEEE transactions on neural networks and learning systems
Word representations, usually derived from a large corpus and endowed with rich semantic information, have been widely applied to natural language tasks. Traditional deep language models, on the basis of dense word representations, requires large mem...

Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.

Journal of the American Heart Association
BACKGROUND: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variabl...