AIMC Topic: Natural Language Processing

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Natural Language Processing Model for Identifying Critical Findings-A Multi-Institutional Study.

Journal of digital imaging
Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the full report and identify all the pertine...

Hyperbolic Deep Neural Networks: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the deep representations in the hyperbolic space provide high fidelity embeddings with few dimensions, especially for data possessing hierarchical structure. Such a hyper...

A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

Annals of emergency medicine
STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represent unrecognized serious medical conditions, including cancer. Recognition of incidental findings freq...

Successful implementation of a nurse-navigator-run program using natural language processing identifying patients with an abdominal aortic aneurysm.

Journal of vascular surgery
BACKGROUND: Abdominal aortic aneurysms (AAA) are often identified incidentally on imaging studies. Patients and/or providers are frequently unaware of these AAA and the need for long-term follow-up. We sought to evaluate the outcome of a nurse-naviga...

Arabic Syntactic Diacritics Restoration Using BERT Models.

Computational intelligence and neuroscience
The Arabic syntactic diacritics restoration problem is often solved using long short-term memory (LSTM) networks. Handcrafted features are used to augment these LSTM networks or taggers to improve performance. A transformer-based machine learning tec...

A Unified Understanding of Deep NLP Models for Text Classification.

IEEE transactions on visualization and computer graphics
The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually. Existing methods cannot meet the need for understanding differ...

Towards Transfer Learning Techniques-BERT, DistilBERT, BERTimbau, and DistilBERTimbau for Automatic Text Classification from Different Languages: A Case Study.

Sensors (Basel, Switzerland)
The Internet of Things is a paradigm that interconnects several smart devices through the internet to provide ubiquitous services to users. This paradigm and Web 2.0 platforms generate countless amounts of textual data. Thus, a significant challenge ...

Semantic Annotation of Experimental Methods in Analytical Chemistry.

Analytical chemistry
A major obstacle for reusing and integrating existing data is finding the data that is most relevant in a given context. The primary metadata resource is the scientific literature describing the experiments that produced the data. To stimulate the de...

Explaining neural activity in human listeners with deep learning via natural language processing of narrative text.

Scientific reports
Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodin...

A natural language processing approach towards harmonisation of European medicinal product information.

PloS one
Product information (PI) is a vital part of any medicinal product approved for use within the European Union and consists of a summary of products characteristics (SmPC) for healthcare professionals and package leaflet (PL) for patients, together wit...